Movement disorder therapy and brain mapping system and methods of tuning remotely, intelligently and/or automatically

ABSTRACT

The present invention relates to methods for remotely and intelligently tuning movement disorder of therapy systems. The present invention still further provides methods of quantifying movement disorders for the treatment of patients who exhibit symptoms of such movement disorders including, but not limited to, Parkinson&#39;s disease and Parkinsonism, Dystonia, Chorea, and Huntington&#39;s disease, Ataxia, Tremor and Essential Tremor, Tourette syndrome, stroke, and the like. The present invention yet further relates to methods of remotely and intelligently or automatically tuning a therapy device using objective quantified movement disorder symptom data and/or brain activity mapped data to determine the therapy setting or parameters to be transmitted and provided to the subject via his or her therapy device. The present invention also provides treatment and tuning intelligently, automatically and remotely, allowing for home monitoring of subjects.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent application Ser. No. 15/280,045 which was filed on Sep. 29, 2016, and which was a continuation of U.S. patent application Ser. No. 14/963,687 which was filed on Dec. 9, 2015 and issued as U.S. Pat. No. 9,522,278 on Dec. 20, 2016, and which was a continuation of U.S. patent application Ser. No. 14/022,376 which was filed on Sep. 10, 2013 and issued as U.S. Pat. No. 9,238,142 on Jan. 19, 2016. U.S. patent application Ser. No. 14/022,376 further claims priority as each of: a continuation-in-part of U.S. patent application Ser. No. 13/918,948, which was filed on Jun. 15, 2013 and issued as U.S. Pat. No. 9,211,417 on Dec. 15, 2015; a continuation-in-part of U.S. patent application Ser. No. 14/022,323, which was filed on Sep. 10, 2013 and issued as U.S. Pat. No. 9,289,603 on Mar. 22, 2016; and a non-provisional application claiming priority to provisional U.S. Patent Application Ser. No. 61/698,890 which was filed on Sep. 10, 2012.

BACKGROUND OF THE INVENTION (1) Field of the Invention

The present invention relates to therapeutic medical apparatus systems, delivery systems, devices and/or methods, systems and methods for mapping a subject's brain and correlating brain regions to particular subject movements, and to apparatus and methods for using neural stimulation to alleviate the symptoms of movement disorders, such as those associated with Parkinson's disease, essential tremor, dystonia, and Tourette's syndrome, including tremor, bradykinesia, rigidity, gait/balance disturbances, and dyskinesia, and also for treating mental health disorders such as major depression, bipolar disorder, and obsessive compulsive disorder for example. The present invention further relates to the use of a movement disorder diagnostic device for automatically and remotely adjusting, or tuning, therapeutic systems, devices, delivery systems, as well as methods thereof.

(2) Technology Review

A current trend in the treatment of diseases identified as being associated with the central nervous system is the stimulation of target areas of the central nervous system to affect therapeutic benefit. Such stimulation has been accomplished with, for example, implanted electrodes that deliver electrical stimulation to target brain regions; one class of electrical neural stimulation devices has been categorized under the name “deep brain stimulation” (DBS). Although the exact neurological mechanisms by which DBS therapies succeed are complex and are not yet fully understood, such therapies have proven effective in treating Parkinson's disease motor symptoms (such as tremor, bradykinesia, rigidity, and gait disturbances), and investigation into the use of DBS for the treatment of this and other neurological and mental health disorders, including major depression, obsessive-compulsive disorder, tinnitus, obesity, criminal tendencies, and antisocial disorders, is ongoing.

Access to movement disorder specialists to assist in diagnosis and treatment of such disorders may require many clinical visits and can be financially burdensome for the geographically disparate subset of the PD population or those unable to travel. Movement disorder center locations can limit access to well-trained clinicians and effective symptom management. Rural patients in one study had a significantly worse quality of life score than their urban counterparts. Telehealth technologies such as home monitoring and online patient data management can have a significant impact on the equity, accessibility, and management of PD for patients who live in rural and remote communities or those unable to travel.

With DBS therapy, electrical pulses characterized by amplitude (volts), current (amps), frequency (Hz), and pulse width (microseconds) are regulated by an implantable pulse generator (IPG) placed beneath the skin. Stimulation affects motor symptoms on the contralateral side, i.e., right side tremor will be treated on the left brain. Although medication is not eliminated, it is typically reduced significantly. DBS efficacy decreases over time as the body adjusts to stimulation and protein buildup around electrode lead attenuates electrical field. Programming sessions are required throughout the patient's lifetime, though the frequency of adjustments is typically greater at first. The IPG typically includes a battery and circuitry for telemetered communication with an external programming device used to adjust, or “tune,” DBS stimulation parameters, which may include, but are not limited to, stimulation frequency, amplitude, pulse width (or wavelength), waveform type, and contact configuration (that is, the selection of which electrodes are utilized from among the electrodes available on a lead, and, if two or more electrodes are active, the relative polarity of each), and the like. These parameters are initially set during implantation surgery separately and independently for each DBS lead that is implanted, and are then further fined-tuned in the outpatient clinic or in a doctor's office following surgery to maximize therapeutic benefit and minimize undesirable stimulation-induced side effects.

DBS programming may be performed by movement disorder neurologists, neurosurgeons, fellows, occupational and physical therapists, nurses, or employees of the DBS manufacturer. However, many patients have inadequate access to DBS programming due to physicians and patients relocating as well as implantations occurring at facilities far from a patient's home. Additionally, there is a shortage of health care professionals highly trained in DBS programming.

The approaches to programming can vary greatly across institutions. Strict iterative procedures whereby initial subjective test results based on human observation are used to determine the effect the parameters have of the patient and new parameters are determined based on those results by clinician calculation and observation are quite time consuming and therefore rarely followed. Many programmers make educated guesses as to the best settings based on their prior experience; however, this experience can vary across institutions and may not take into account varied lead positioning. Many programmers simply ignore bipolar or tripolar configurations whereby stimulation is provided from two or three contacts on a single DBS lead simultaneously, and do not adjust frequency or pulse width in an attempt to speed the programming process; however, neglecting these options can lead to suboptimal patient outcomes. In constant-voltage IPGs, the voltage of each pulse is set, but the current will automatically change based on the electrode impedance. This leads to variable amounts of current being delivered the stimulation target as impedances change. Additionally, since impedances will vary across electrode contacts, applying the same voltage on two different contacts will likely lead to different therapeutic currents being delivered. On the other hand, constant-current IPGs specify the current to be delivered and adjust the voltage accordingly based on the impedance. Since the therapeutic effects of DBS are based on current delivered at a given target, constant-current IPGs are preferable to constant-voltage IPGs.

While the above-described equipment and procedures are typical as of the filing of this application, variations and refinements may become commonplace as neural implant technology advances. Conceivably, uses of a multiplicity of DBS leads or networks of DBS leads may provide greater coverage, enabling the stimulation of larger and more varied target areas, and miniaturization and improved telemetry may obviate the need for the extension cable and/or the IPG altogether as leads become self-powering and/or self-controlling or permit for built-in telemetry. Advances in nanotechnology and materials may also allow DBS leads in the future to become self-repositioning, self-cleaning, or resistant to biological rejection for improved long-term therapeutic operation and more precisely targeted implantation.

The current standard in evaluating the severity of movement disorder symptoms in Parkinson's disease is the manually human scored Unified Parkinson's Disease Rating Scale (UPDRS) used to score motor tests, many of which involve repetitive movement tasks such as touching the nose and drawing the hand away repeatedly, or rapidly tapping the fingers together. A battery of exercises, typically a subset of the upper extremity motor section of the UPDRS, is normally completed during DBS lead placement surgery and subsequent programming sessions to evaluate performance while a clinician qualitatively assesses symptoms. Each test is typically evaluated by a clinician based solely on visual observation and graded on a scale that typically ranges from 0 (minor) to 4 (severe).

Postoperatively, assessing DBS response and reprogramming stimulation parameters require a significant time commitment. Several stimulation parameters can be modified, including, but not limited to, electrode polarity, amplitude, current, pulse width, waveform type, and frequency. DBS programming and patient assessment may be performed by a variety of healthcare professionals, including movement disorder neurologists, neurosurgeons, fellows, occupational and physical therapists, nurses, and employees of the DBS manufacturer. Stimulation optimization is typically performed based on results of an exam such as the UPDRS, with the patient in four states (off medication/off DBS, off medication/on DBS, on medication/off DBS, and on medication/on DBS). The process of DBS adjustment is iterative and largely involves trial-and-error. Programming and patient assessment from preoperatively to one year after surgery requires approximately 30 hours of nursing time per patient.

Clinicians presently lack tools that combine physiological, electrical, and behavioral data to optimize electrode placement and stimulator programming. Optimizing electrode placement and stimulation parameters improves patient outcome by alleviating motor symptoms and minimizing complications. The present invention addresses this need for improved electrode placement and adjustment of deep brain stimulation parameters by providing a repeatable, automated or semi-automated tool that can assist stimulation parameter tuning during surgical electrode placement and outpatient programming sessions. In particular, the present invention aims to provide methods for the collection and transmission of objective biokinetic data, which data is then processed to output objective movement disorder symptom severity measures on a continuous scale in real-time to guide clinician decision making. The improved resolution and repeatable results of the present invention should reduce time and costs of DBS procedures as well as improve patient outcomes.

Subject movement of virtually all types: voluntary, involuntary, normal, symptomatic, etc., typically and generally originates with a signal from the subject's brain or spinal cord. All movements, from basic movements, such as walking or waving one's hand, to complex movements, such as playing a sport or a musical instrument, require coordination and communication between the subject's brain and the rest of the subject's body, in real time, where the brain initiates and controls the movements of the body to perform the particular movement—either consciously or subconsciously. Typically, body movement is controlled largely by the motor cortex region of the brain, but the motor cortex is not necessarily employed for all movements in all subjects, or in all situations. Basic movements may still be performed, if, for example, the motor cortex is damaged or injured. However, complex voluntary movements almost always require sophisticated and complex operation of the motor cortex to direct the body's movements. Essentially, the type of movement being performed activates and requires excitation of different portions of the brain, even different portions of the motor cortex itself, and further even different combinations of individual neurons within the motor cortex or other parts of the brain. Thus, movement is not controlled or governed based on the operation of each individual neuron, but rather the complex interactions of individual neurons in relation to each other in order to perform different combinations of movements. Further complicating the correlation of the brain's control over movement is the introduction of injury or disease states, for example movement disorders, which lead to abnormal or changed interactions of neurons and symptomatic movements. Symptomatic movement may be caused by any number of sources, including, but not limited to faulty brain function, interrupted signal transmission, damaged receptors, and the like. The complex interactions between neurons do not end in the brain, but rather continue throughout the neurons in the subject's entire body. In order to move one's foot, particular neurons are employed in the subject's brain, and the signal to move the foot must be transmitted through neurons all the way to the subject's foot in order to cause the foot to move. Thus, even the most simple of movements is the result of such a chain of interactions between neurons, which makes the process of mapping brain activity related to movement even more difficult to ascertain. Although the nervous system typically has numerous redundancies or alternative routes built into the neurological pathways, it may require as few as one neuron misfiring anywhere in the chain, for any reason, to generate symptomatic movement in any part of the subject's body. Further, each individual subject's nervous system anatomy is unique, so the same symptom in two different subjects, may originate from an entirely different source in each of the subjects. Brain mapping, the study of the anatomy and function of the brain and spinal cord, allows for the correlation of an individual subject's brain function with corresponding results distal from the brain—such as movement. In other words, brain mapping allows for identification of the portion, region, or structure of the subject's brain that corresponds to a particular movement or symptom. It follows, then, that such mapping and correlation would allow for more accurately targeted therapeutic stimulation. Brain mapping can generally be performed by any number of methods including imaging (both non-invasive and invasive techniques are used), immunohistochemistry, molecular and optogenetics, engineering, neurophysiology and nanotechnology means, with imaging, or more specifically neuroimaging, being the most common and most efficacious. Numerous neuroimaging methodologies are known and have been used to track brain activity and help to map it, including, but not limited to, cortical stimulation mapping, diffusion magnetic resonance imaging (dMRI), electroencephalogram (EEG), electrocorticography, functional magnetic resonance imaging (fMRI), medical image computing, and positron emission tomography (PET). Neuroimaging methods of brain mapping are used to obtain brain imaged that are supplemented and augmented by results of additional data (such as other imaging results or non-imaging data) that serve to project various behavior, or measures of behavior onto the brain regions or structures from with the initiate. An example of such a brain map result is called a connectogram, and depicts cortical regions of the subject's brain around a circle where the cortical regions are organized according to the lobe of the brain where the region is located, and where concentric circles within the ring are representative of common neurological measurements (e.g., cortical thickness or curvature), and weighted lines are used to connect the various concentric circles indicating connections between the various cortical regions, the weight being indicative of the strength of the connection. Using such a map, a subject's movement can be correlated to the several portions of the brain that contribute to that movement, and the strength of the correlation may be shown. However, many brain mapping techniques known in the art exhibit a very low resolution, sometimes including hundreds of thousands of neurons in a single voxel of the image. This is clearly due in large part to the complex and complicated nature of the nervous system. It is also due in part to the fact that each individual function of the subject (not just movement) involves input or excitation of several parts of the brain. That is to say, liftings one's arm may not be solely attributable to an individual or small group of neurons located on the motor cortex for all or even many individuals. Examples of such traditional brain mapping systems and techniques are known in the art, for example in U.S. Pat. No. 7,706,871 to Devlin et al., U.S. Pat. No. 4,421,122 to Duffy, U.S. Pat. No. 4,862,359 to Trivedi et al., U.S. Pat. No. 4,649,482 to Raviv et al., and U.S. Pat. No. 4,844,086 to Duffy, as well as U.S. Patent Publication Serial no. 2002/0099295 to Gil et al., all of whose disclosures are herein incorporated by reference. Converse to the systems and methods known in the art, however, highly accurate brain mapping often requires high-resolution work focused on individual brain circuits, or at least, small groups of such circuits. In other words, brain mapping is a highly sensitive and computationally intense procedure, that is not easily and readily performed in an accurate manner alone, and even less so while adding additional variables such as sensor input and stimulation from devices such as therapeutic devices (e.g., functional electrical stimulation (FES), DBS, and the like).

It is therefore an object of the present invention to provide a system for screening patients for viability of DBS therapy prior to extensive, repetitive travel and expense, and prior to requiring surgical implantation of DBS leads. It is further an object of the present inventions to provide such a screening system to help minimize healthcare costs and to prevent adverse effects in patient quality of life associated with ineffective or unnecessary surgery, and to help clinicians to better select courses of treatment for patients. It is further an object of the present invention to couple at-home patient viability screening and automatically-assigned quantitative motor assessments with procedures and practices for DBS implantation and parameter tuning and programming in semi-automatic and automatic ways to provide improved and less costly movement disorder patient therapy, including but not limited to remote, semi-automatic and automatic titration and control of therapeutic actuators and treatment devices. It is still further an object of the present invention to provide brain mapping capability that is functionally accurate and subject-specific, particularly related to the subject's movement and symptoms of movement disorders. It is yet still a further object of the present invention to use the brain mapping functionality to increase the accuracy and minimize the stimulation required in order to tune the subject's therapy device.

It is further an object of the present invention to provide automated functional mapping based on objective motor assessments and algorithms for resolving an optimal set of programming parameters out of the thousands of possibilities to provide an expert system to enable programming at a local medical facility. The system is designed for use by any healthcare professional, but is particularly aimed at allowing a general practitioner or nurse with minimal training or experience in DBS programming and disease management to increase access to high-quality postoperative DBS management. The system will minimize the required expertise of the clinician by requiring little or no advanced knowledge of complex neurophysiology or Mill imaging.

Existing systems for quantifying Parkinson's disease motor symptoms are described in this application's parent applications (Provisional U.S. Patent Application Ser. No. 61/698,890, U.S. Pat. Nos. 9,211,417, and 9,289,603), which are herein incorporated by reference, and which describe novel systems for measuring motor dysfunction symptoms and computing measures based on UPDRS scores therefrom. Additionally, the present invention and system may benefit from similar and related systems, methods and devices such as those described in U.S. patent application Ser. No. 13/152,963, U.S. patent application Ser. No. 13/185,287, U.S. Pat. No. 8,702,629, U.S. patent application Ser. No. 11/432,583, U.S. patent application Ser. No. 12/250,792, U.S. Pat. Nos. 8,679,038, 8,845,557, and U.S. patent application Ser. No. 13/785,273, which are hereby incorporated by reference. Preferably, the system and methods described therein are incorporated, in whole or in part, into the present invention as a means of automatic symptom quantification. The resultant scores objectively quantify movement disorder symptoms advantageously using a scale that is familiar to clinicians.

SUMMARY OF THE INVENTION

The present invention relates to methods for semi-automatically and automatically adjusting, or tuning, treatment parameters in movement disorder therapy devices and systems, and more specifically for optionally remotely and intelligently adjusting or tuning such devices and systems. Semi-automatic adjustment includes providing the clinician, physician or technician with objective, quantitative or semi-quantitative data or measurements related to a subject's movement disorder symptoms, determining desired parameters, and then remotely entering those parameters either semi-automatically or automatically into the subject's therapy device. Semi-automatic or automatic adjustment (including but not limited to remote adjustment by clinicians) includes for example providing data including, but not limited to, objective, quantitative or semi-quantitative data and/or measurements related to a subject's movement disorder symptoms to an algorithm, using the data or measurements for determining desired parameters using the algorithm, and then entering those parameters either semi-automatically (i.e., allowing clinician, physician or technician to review and/or approve/adjust) or to automatically into the subject's therapy device. Alternatively or in addition, semi-automatic or automatic adjustment may not require sensors to provide data and/or measurements related to a subject's movement disorder symptoms, but rather may use qualitative or quantitative information such as, for example, traditional telemedicine techniques or remote observation by a clinician, physician or technician via video connection. In such embodiments, the clinician, physician or technician would perform a more classic scoring of the subject's symptoms rather than using quantified data and/or measurements from sensors attached to the subject's body or device such as for example by using video links between the clinician and the patient or subject. Remote adjustment or tuning includes the control and or decision regarding the therapy parameters to be programmed into the subject's therapy device being located remotely from the subject. For example, remote adjustment or tuning may be controlled by a clinician, physician or technician or an automated or semi-automated system using data transmitted from the subject and/or his or her therapy device without the subject having to travel to the clinical site to for such data to be obtained or transferred. The present invention further relates to a system for screening patients to determine if they are viable candidates for certain therapy modalities. The present invention still further provides methods of quantifying movement disorders for the treatment of patients who exhibit symptoms of such movement disorders including, but not limited to, Parkinson's disease and parkinsonism, Dystonia, Chorea, and Huntington's disease, Ataxia, Tremor and Essential Tremor, Tourette syndrome, and the like. The present invention yet further relates to methods of automatically and intelligently tuning a therapy device using objective quantified movement disorder symptom data acquired by a movement disorder diagnostic device with the therapy settings or parameters to be provided to the subject via his or her therapy device.

Objective measurement and quantification of a subject's movement disorder symptoms, which is a preferable embodiment of the present invention includes symptoms such as tremor, bradykinesia, dyskinesia, gait and/or balance disturbances, and the like requires, as a first step, a measurement of the movement. This measurement can be performed by measuring a single movement metric, different movement metrics, or a combination of a number of movement metrics; and the movement metric or metrics being measured may include linear or rotational displacement, velocity, or acceleration, or any other metric that could give a quantitative indication of motion; and the part of the body being measured for motion may be a limb (as at a wrist, ankle, or finger) or may be the trunk of the body (as at a shoulder or torso), and the head. Sensors used for measuring body movement or motion include gyroscopes and accelerometers, preferably miniaturized, electromagnets, video, a multitude of sensors or system disclosed herein, or other sensors known to those skilled in the art. Additionally, sensors for measuring physiological signals such as electromyogram (EMG), electrooculogram (EOG), electroencephalogram (EEG), electrocardiogram (EKG), electrocorticogram (ECoG), or other physiological signals which can directly or indirectly measure movement metrics in the subject may be included if such sensors and signals may be used to sense, detect, measure, and/or quantify the subject's external body motion, or related aspects. Other systems that can be used to detect and measure body motion include motion capture systems, machine vision systems, sonic or laser Doppler velocity transducers, infrared systems, GPS, or any other system known to those skilled in the art. The movement disorder diagnostic device used in the present invention may incorporate one or more of any of the above sensors or systems. Currently used movement data acquisition and diagnostic systems, such as described in U.S. Pat. No. 8,187,209, herein incorporated by reference, may similarly be used in certain embodiments of the present invention. In the present disclosure, “movement data” is construed as including, but not being limited to, any signal or set of signals, analog or digital, corresponding to movement of any part of the body or multiple parts of the body, independently or in conjunction with each other. This includes physiological signals from which movement data or symptoms can be derived. Preferably, this movement data is generated with a movement sensor such as for example a gyroscope and/or an accelerometer, and additionally or optionally a video sensor.

Movement may be continuously measured over long time spans, or may be measured only over a short time span, for example, as during the period of one or more tests taken from or based on the UPDRS motor exam. A measurement time period comprises two separate time periods: (i) a sensing time during which the movement disorder diagnostic device and its included sensors are used to sense and measure the subject's external physical motion; and (ii) a processing or calculation time wherein the measured motion data is used to calculate objective scores and/or other kinematic data that quantify the severity of the subject's movement disorder symptoms and side effects, and wherein the scores. The measurement time required to adequately and accurately sense, measure and quantify the subject's movement can depend on the particular movement test or task being performed, which typically corresponds to a particular symptom of a movement disorder. Generally, however, the system aims to minimize the amount of measurement time required to obtain sufficient movement data and provide quantitative scores and/or kinematic data to continue the evaluative process. Preferably, the measurement time required to provide objective scores and/or kinematic data is less than about 120 minutes. More preferably, the measurement time required to provide objective scores and/or kinematic data is less than about 90 minutes. Still more preferably, the measurement time required to provide objective scores and/or kinematic data is less than about 60 minutes. Yet more preferably, the measurement time required to provide objective scores and/or kinematic data is less than about 45 minutes. Even more preferably, the measurement time required to provide objective scores and/or kinematic data is less than about 30 minutes. Still yet more preferably, the measurement time required to provide objective scores and/or kinematic data is less than about 15 minutes. Still even more preferably, the measurement time required to provide objective scores and/or kinematic data is less than about 10 minutes. Yet still more preferably, the measurement time required to provide objective scores and/or kinematic data is less than about 5 minutes. Yet even more preferably, the measurement time required to provide objective scores and/or kinematic data is less than about 60 seconds. Even still more preferably, the measurement time required to provide objective scores and/or kinematic data is less than about 30 seconds. Even yet more preferably, the measurement time required to provide objective scores and/or kinematic data is less than about 15 seconds. Still even yet more preferably, the measurement time required to provide objective scores and/or kinematic data is less than about 1 second.

Many embodiment utilize separate and discrete time periods for measuring movement, brain activity, or both in several iterations. Typically, such discrete time periods (example a first, second, third, etc.) are used for obtaining measurements to train an algorithm for ongoing and future use of the devices, systems, and methods of the present invention. For example, in some embodiments measurements are taken during a first time period, treatment or therapy may be adjusted, and measurements may be taken during a second time period. Effectively, such second time period measurements can be use to determine the effect of the adjustments to the treatment or therapy parameters. Such adjustment preferably leads to a decrease in symptoms or side effects. In any event, such measurement during iterative time periods may be performed to obtain a set of data which correlates treatment or therapy parameters to resultant measurements, and thus the algorithm(s) may be trained in order to associate certain parameters with certain outcomes. Thus, over time, the algorithm may be trained to require fewer types of measurements in order to provide the best treatment or therapy to address the subject's symptoms or side effects. As a specific, though non-limiting, example, during the iterative measurement time periods, the system may measure both movement (including symptoms and/or side effects of treatment or therapy) and brain activity of the subject. The iterative training process allows the algorithm to be trained in the correlation between such movement and brain activity. In time, movement may no long need to be measured during every measurement time period, or at all, and the system may operate only on measured brain activity by which the trained algorithm would recognize the symptoms or side effects based on such measured brain activity, but would not require measured movement, and can then generate treatment or therapy parameters to address those symptoms or side effects.

In some embodiments, a periodic system may be employed wherein the subject's external body motions are sensed, measured, and quantified repeatedly but at predefined or altering intervals. In such periodic embodiments, the periodic measurements preferably conform to the above described measurement time period standards. Embodiments utilizing periodic measurement may begin when the subject attaches or dons the movement disorder diagnostic device, and may involve a step of instructing the subject to attach or don the device to begin a measurement period. Protocols for periodic measurement may be envisioned wherein a subject follows a particular schedule for measurement and quantification of movement disorder data, and wherein the schedule may change throughout the course of treatment and/or therapy. In still other embodiments, a continuous monitoring system may be employed wherein the movement disorder diagnostic device continuously senses, measures and quantifies the subject's external body movements over extended periods of time, such as hours, days, weeks or months. Preferably, in the continuous measurement embodiments, the diagnostic device senses, measures and/or quantifies the subject's external body movements substantially continuously, with no breaks or stoppages in its operation. However, the limits of continuous operation may be defined by characteristics of the device, such as battery life, form factor and construction (e.g., if it needs to be removed to shower), and other such concerns.

The movement disorder diagnostic device contains at least one electronic component that further may contain internal or onboard memory for storage of the movement data such that the data may be transferred at a later time. More preferably, the movement disorder diagnostic device further may contain communications electronics, which transmit the movement data to an external device for storage and/or analysis. The communication electronics preferably is/are wireless, and most preferably is/are radio frequency wireless. The external device may be a centralized storage database, parallel databases, a cloud-based database, a computer, tablet, cell phone including for example smartphone, personal data assistant (PDA) or similar device, or a combination of database and computer or communication devices. Preferably, such transmission of data occurs substantially in real-time. By real-time, it is meant that preferably, data is transmitted within 30 minutes of being acquired, measured, or calculated. More preferably, data is transmitted within 20 minutes of being acquired, measured, or calculated. Still more preferably, data is transmitted within 10 minutes of being acquired, measured, or calculated. Yet more preferably, data is transmitted within 5 minutes of being acquired, measured, or calculated. Even more preferably, data is transmitted within 5 minutes of being acquired, measured, or calculated. Still yet more preferably, data is transmitted within 3 minutes of being acquired, measured, or calculated. Even yet more preferably, data is transmitted within 60 seconds of being acquired, measured, or calculated. Yet still more preferably, data is transmitted within 45 seconds of being acquired, measured, or calculated. Yet even more preferably, data is transmitted within 30 seconds of being acquired, measured, or calculated. Even still more preferably, data is transmitted within 15 seconds of being acquired, measured, or calculated. Even yet more preferably, data is transmitted within 5 seconds of being acquired, measured, or calculated. Still even yet more preferably, data is transmitted within 1 second of being acquired, measured, or calculated. Yet even still more preferably, data is transmitted substantially simultaneously within milliseconds of being acquired, measured, or calculated.

Following measurement of symptomatic movement, the next step in objective quantification of a subject's movement disorder symptoms is the extraction of statistical kinematic features from the acquired movement data via processing. This processing may take place during or following data acquisition and may occur within a movement data acquisition device or within a different processing device, such as a personal computer, PDA, smart phone, tablet computer, touch screen interface, or the like, with which the acquisition device interfaces, either through a cable connection or by wireless transmission. Useful kinematic features that may be extracted from gyroscopic data may include, for example, peak power angular velocity, peak power angle, RMS angular velocity, frequency, maximum amplitude, maximum peak-to-peak amplitude, mean angular velocity, and wavelet parameters, as well as the covariance or standard deviation over time of any of these metrics. Useful kinematic features that may be extracted from accelerometer data may include, for example, peak power acceleration, peak power velocity, peak power position, RMS acceleration, RMS velocity, RMS position, frequency, maximum amplitude, maximum peak-to-peak amplitude, mean acceleration, and wavelet parameters, as well as the covariance or standard deviation over time of any of these metrics. In a movement data acquisition system, or movement disorder diagnostic measuring apparatus, that combines a three-axis accelerometer and a three-axis gyroscope to produce 6 channels of movement data, one or any combination of the above kinematic features can be extracted from any of the 6 kinematic channels to be used as inputs to a trained scoring algorithm in the next step. The listed kinematic features for the sensors above are intended to be exemplary, and not limiting; other types of sensors will produce different data from which different sets of features may be extracted.

The trained scoring algorithm used to process the kinematic features extracted from the movement data may comprise, for example, one or more of a simple or multiple linear regression, an artificial neural network, a Bayesian network, or a genetic algorithm. The output of the trained scoring algorithm may be a single score or multiple scores of any scale; a single score on the same scale as that of the UPDRS may be preferred in certain applications where simplicity or familiarity is the paramount concern, while more sophisticated scores and scales may be preferred for other advanced applications, such as those that involve recommendations for treatment or closed-loop automated treatment delivery.

In various embodiments, following the step of symptom quantification, a separate tuning algorithm may compute suggested changes to the therapy system parameter settings based on the result of the symptom quantification algorithm and known or predicted current therapy system parameter settings and physiological models.

Depending on the embodiment of the invention, the current therapy system parameter settings changes may be input into the algorithm by a software user interface (integrated tuning), or may be automatically sensed and input from the DBS parameter settings by communicating with a DBS implant or its programmer device or unit (intelligent tuning), or may be known because the DBS parameter settings have been preset to some known baseline settings or restored to a previously saved settings preset. The existing parameter settings might also be predicted or derived based, for example, on observed or measured therapy effectiveness. Suggested therapy system parameter settings changes are then input into the therapy system, and their effectiveness is measured using the above-described method of symptom quantification.

The process of tuning therapy system parameter settings may remain iterative, but the present invention significantly minimizes, or at least greatly reduces the time and expertise required to arrive at optimized stimulation or therapy parameter settings, advantageously allowing clinicians, technicians or physicians with lesser training or experience to adjust parameter settings during patient visits, and to do so in less time than is currently required. Additionally, the present invention increases access to geographically disparate populations by putting the expertise into the system and reducing or eliminating the need for an expert or trained clinician to be present with each subject.

Many embodiments of the present invention utilize a remote tuning or adjustment system. In such embodiments, at least one electronic component for transmitting and receiving signals is required. In such embodiments, data corresponding to the subject's measured and quantified movement and symptom data may be collected by the movement disorder diagnostic device and transmitted using the at least one electronic component for transmitting signals to a remote location or remote locations. The data may be transmitted to a clinical center or location where a clinician, physician or technician can view the data. In such embodiments, the clinician, physician or technician can then make a decision and determination regarding a second level of therapy settings that should be applied to the subject's therapy device. Alternatively or in addition, an algorithm may be used to provide the determination as to the second level of therapy parameters to be applied to the subject's therapy device, and a clinician, physician or technician may optionally review the settings determined by the algorithm. In the remote tuning or adjustment embodiments, once a determination as to a second or next level of therapy parameters is made, this second or next level of parameters is then transmitted back to the subject's therapy device where it is received by at least on electronic component for receiving signals. In still other remote embodiments, a tuning algorithm, as described below, of the movement disorder diagnostic device may provide suggested or determined second or next levels of therapy parameters, and in such embodiments the movement data and/or such suggested or determined parameters may be transmitted to storage or other remote locations as described. Additionally, the movement data and/or second or next level of therapy parameters may additionally be transmitted to a central server, cloud based server, or other such database for storage and backup purposes.

Many embodiments of the present invention include optimization or tuning algorithm(s) which are used to determine or recommend optimum therapy settings or parameters. Such optimization algorithms may include, but are not limited to simplex algorithms, extensions of the simplex algorithm designed for quadratic and/or linear function programming, combinatorial algorithms, and other multi-variant optimization algorithms known to those in the art. In order to determine what a desired or optimal level of therapy parameters might be, the subject's symptoms or side effects must first be measured and quantified. The measurement and quantification preferably take place while the subject is performing at least one movement disorder test as instructed. Once the initial measurement and quantification has been obtained, the system and/or, in some embodiments a clinician, physician or technician, programs a second level of therapy parameters into the subject's therapy device, and the subject repeats the movement disorder test(s) while the symptoms or side effects are again measured and quantified. This process is repeated until the desired result(s), goals or constraints are achieved. These processes and steps are described in greater detail below. Preferably, whether obtaining optimized therapy parameters or settings, or when iteratively testing to determine a second level of therapy parameters, preferably, the subject is instructed to perform, and performs, at least 1 movement disorder test, where the test comprises at least one task related to the subject's external body motion. More preferably, the subject is instructed to perform, and performs, at least 2 movement disorder tests. Still more preferably, the subject is instructed to perform, and performs, at least 3 movement disorder tests. Yet more preferably, the subject is instructed to perform, and performs, at least 4 movement disorder tests. Even more preferably, the subject is instructed to perform, and performs, at least 5 movement disorder tests. Still yet more preferably, the subject is instructed to perform, and performs, at least 6 movement disorder tests. Even still more preferably, the subject is instructed to perform, and performs, at least 7 movement disorder tests.

Optimization of stimulation or therapy parameters or settings can be described in reference to various constraints or desired results. In some embodiments, optimization, or the level of parameters or setting selected based at least in part on movement disorder tests, results and scores refers to a reduction or minimization of symptom occurrence and/or severity. Preferably in such embodiments, an optimized or second level of therapy parameters or settings corresponds to at least a 10% reduction in the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy. More preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 20% reduction in the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy. Yet more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 30% reduction in the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy. Still more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 40% reduction in the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy. Even more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 50% reduction in the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy. Still yet more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 60% reduction in the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy. Even yet more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 70% reduction in the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy. Yet still more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 75% reduction in the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy. Even still more preferably, an optimized or second level of therapy parameters or settings corresponds to at least an 80% reduction in the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy. Yet even more preferably, an optimized or second level of therapy parameters or settings corresponds to at least an 85% reduction in the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy. Still even more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 90% reduction in the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy. Yet still even more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 95% reduction in the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy. Most preferably, an optimized or second level of therapy parameters or settings corresponds to substantially eliminating the occurrence and/or severity of the subject's symptoms while the subject is receiving therapy or is under the effects of recently received therapy.

In other embodiments, optimization, or the level of parameters or setting selected based at least in part on movement disorder tests, results and scores refers to a reduction or minimization of side effect occurrence and or severity. Side effects may be a result of pharmaceutical therapy (medication) the subject is receiving to treat his or her movement disorders, or from the stimulation therapy (e.g., DBS). Preferably in such embodiments, an optimized or second level of therapy parameters or settings corresponds to at least a 10% reduction in the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy. More preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 20% reduction in the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy. Yet more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 30% reduction in the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy. Still more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 40% reduction in the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy. Even more preferably, an optimized or second level of therapy to parameters or settings corresponds to at least a 50% reduction in the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy. Still yet more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 60% reduction in the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy. Even yet more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 70% reduction in the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy. Yet still more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 75% reduction in the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy. Even still more preferably, an optimized or second level of therapy parameters or settings corresponds to at least an 80% reduction in the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy. Yet even more preferably, an optimized or second level of therapy parameters or settings corresponds to at least an 85% reduction in the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy. Still even more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 90% reduction in the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy. Yet still even more preferably, an optimized or second level of therapy parameters or settings corresponds to at least a 95% reduction in the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy. Most preferably, an optimized or second level of therapy parameters or settings corresponds to substantially eliminating the occurrence and/or severity of the subject's side effects while the subject is receiving therapy or is under the effects of recently received therapy.

Preferably, where the desired result is to reduce or minimize the subject's movement disorder symptoms, the optimized or second level of therapy parameters results in a reduction or minimization of at least 1 movement disorder symptom. More preferably, the optimized or second level of therapy parameters results in a reduction or minimization of at least 2 movement disorder symptoms. Still more preferably, the optimized or second level of therapy parameters results in a reduction or minimization of at least 3 movement disorder symptoms. Yet more preferably, the optimized or second level of therapy parameters results in a reduction or minimization of at least 4 movement disorder symptoms. Even more preferably, the optimized or second level of therapy parameters results in a reduction or minimization of at least 5 movement disorder symptoms.

Preferably, where the desired result is to reduce or minimize the subject's side effects from medication or therapy, the optimized or second level of therapy parameters results in a reduction or minimization of at least 1 side effect. More preferably, the optimized or second level of therapy parameters results in a reduction or minimization of at least 2 side effects. Still more preferably, the optimized or second level of therapy parameters results in a reduction or minimization of at least 3 side effects. Yet more preferably, the optimized or second level of therapy parameters results in a reduction or minimization of at least 4 side effects. Even more preferably, the optimized or second level of therapy parameters results in a reduction or minimization of at least 5 side effects.

Other secondary constraints or desired results may also be considered when optimizing or determining a second level of therapy parameters or settings such as maximizing the battery life of the therapeutic (e.g., DBS) device, maximizing the therapeutic window, and the like. Such constraints or desired results as these are secondary only in that the primary goal of the therapy is to increase the subject's quality of life by reducing or minimizing symptoms or side effects, or balancing both, while also trying to improve the duration and quality of therapy otherwise. For example, maximizing battery life of the therapy device helps to increase the time required between subject's visits to the clinician, physician or technician as well as ensuring that the device has sufficient power and capability to effectively provide the determined levels of therapy. Similarly with maximizing the therapeutic window, which also increases the time between visits, but also maximizes the length of time that the stimulation therapy has a positive effect on the subject and reducing the number of stimulations required to achieve the desired results. Typically, the subject and his or her clinician, physician or technician will agree upon the primary desired result, such as minimizing symptoms, but numerous other such constraints will also be considered, weighed and balanced in determining the optimized or second level of parameters or settings.

Several embodiments may include a general optimization strategy in which combinations of the above desired results or constraints are used to select the appropriate optimized settings. For example, such embodiments may optimize based on reducing or minimizing both symptoms and side effects. Any combination of type and/or number of desired results or constraints may be used to optimize the system. Preferably, at least two different desired results or constraints are considered when determining an optimized group of therapy settings or parameters. More preferably, at least three different desired results or constraints are considered when determining an optimized group of therapy settings or parameters. Still more preferably, at least four different desired results or constraints are considered when determining an optimized group of therapy settings or parameters. Yet more preferably, at least five different desired results or constraints are considered when determining an optimized group of therapy settings or parameters. Even more preferably, at least six different desired results or constraints are considered when determining an optimized group of therapy settings or parameters. Most preferably, more than seven different desired results or constraints are considered when determining an optimized group of therapy settings or parameters.

Numerous embodiments of the present invention are envisioned in this disclosure. These following embodiments are examples of the many embodiments encompassed by the present invention, but do not in any way limit the many other embodiments covered by this disclosure.

One embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of providing a movement disorder diagnostic device to a subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, and a processor comprising an algorithm, instructing the subject to perform at least one movement disorder test(s) while the subject is undergoing DBS therapy or is under the effects of DBS therapy, measuring and quantifying motor symptoms of the subject based at least in part on the signal from the at least one physiological or movement sensor(s) during the at least one movement disorder test(s), entering data corresponding to the subject's measured and quantified motor symptoms into an algorithm, providing, with the algorithm, a second level of DBS parameters based at least in part on the data entered into the algorithm, and entering the second level of DBS parameters into the subject's DBS device such that the subject's DBS device operates under the second level of DBS parameters.

Another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of providing a movement disorder diagnostic device to a subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, and a processor comprising an algorithm, instructing the subject to perform at least one movement disorder test(s) while the subject is undergoing DBS therapy or is under the effects of DBS therapy, measuring and quantifying motor symptoms of the subject based at least in part on the signal from the at least one physiological or movement sensor(s) during the at least one movement disorder test(s), entering data corresponding to the subject's measured and quantified motor symptoms into an algorithm, determining, with the algorithm, at least two optional groups of DBS parameters, each optional group of parameters corresponding to a different desired outcome or constraint, selecting one of the at least two optional groups of DBS parameters, and entering the selected optional group of DBS parameters into the subject's DBS device such that the subject's DBS device operates under the second level of DBS parameters.

Still yet another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of providing a movement disorder diagnostic device to a subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, and a processor comprising an algorithm, instructing the subject to perform at least one movement disorder test(s) while the subject is undergoing DBS therapy or is under the effects of DBS therapy, measuring and quantifying motor symptoms of the subject based at least in part on the signal from the at least one physiological or movement sensor(s) during the at least one movement disorder test(s), entering the data corresponding to the subject's measured and quantified motor symptoms into an algorithm, determining, with the algorithm, at least two optional groups of DBS parameters, each optional group of parameters corresponding to a different desired outcome or constraint, transmitting data corresponding to the subject's measured and quantified motor symptoms and the at least two optional groups of DBS parameters to a clinician, physician or technician—at a remote location, having the clinician, physician or technician select at least two of the at least two optional groups of DBS parameters, combining the at least two selected optional groups of DBS parameters into one set of combined DBS parameters, transmitting the set of combined DBS parameters to the subject's DBS device, and entering the set of combined DBS parameters into the subject's DBS device such that the subject's DBS device operates under the second level of DBS parameters.

Yet still another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of providing a movement disorder diagnostic device to a subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, and a processor comprising an algorithm, instructing the subject to perform at least one movement disorder test(s) while the subject is undergoing DBS therapy or is under the effects of DBS therapy, measuring and quantifying motor symptoms of the subject based at least in part on the signal from the at least one physiological or movement sensor(s) during the at least one movement disorder test(s), entering data corresponding to the subject's measured and quantified motor symptoms into an algorithm, determining, with the algorithm, at least two optional groups of DBS parameters, each optional group of parameters corresponding to a different desired outcome or constraint, transmitting data corresponding to the subject's measured and quantified motor symptoms and the at least two optional groups of DBS parameters to a clinician, physician or technician at a remote location, having the clinician, physician or technician select one of the at least two optional groups of DBS parameters, and uploading, substantially simultaneously with transmitting, with at least one electronic component the selected optional group of DBS parameters and/or measured and quantified motor symptoms to a database for storage and/or review by a clinician, technician or physician.

Even yet another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of providing a movement disorder diagnostic device to a subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, and a processor comprising an algorithm, instructing the subject to perform at least one movement disorder test(s) while the subject is undergoing DBS therapy or is under the effects of DBS therapy, measuring and quantifying motor symptoms of the subject based at least in part on the signal from the at least one physiological or movement sensor(s) during the at least one movement disorder test(s), entering data corresponding to the subject's measured and quantified motor symptoms into an algorithm, determining, with the algorithm, at least two optional groups of DBS parameters, each optional group of parameters corresponding to a different desired outcome or constraint, selecting one of the at least two optional groups of DBS parameters, and uploading, substantially simultaneously with transmitting, with at least one electronic component the second level of DBS parameters and/or measured and quantified motor symptoms to a database for storage and/or review by a clinician, technician or physician.

Yet still another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of providing a movement disorder diagnostic device to a subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, and a processor comprising an algorithm, instructing the subject to perform at least one movement disorder test(s) while the subject is undergoing DBS therapy or is under the effects of DBS therapy, measuring and quantifying motor symptoms of the subject based at least in part on the signal from the at least one physiological or movement sensor(s) during the at least one movement disorder test(s), entering data corresponding to the subject's measured and quantified motor symptoms into an algorithm, determining, with the algorithm, at least two optional groups of DBS parameters, each optional group of parameters corresponding to a different desired outcome or constraint, selecting at least two of the at least two optional groups of DBS parameters, combining the at least two selected optional groups of DBS parameters into one set of combined DBS parameters, and uploading, substantially simultaneously with transmitting, with at least one electronic component the second level of DBS parameters and/or measured and quantified motor symptoms to a database for storage and/or review by a clinician, technician or physician.

Yet even still another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of providing a movement disorder diagnostic device to a subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, and a processor comprising an algorithm, displaying on a programming device a list of activities, actions or tasks for the subject to select from, having the subject elect at least one activity, action or task from the list on the programming device, selecting with the movement disorder diagnostic device a predetermined set of DBS parameters corresponding to the elected at least one activity, action or task, entering with the programming device the group of selected DBS parameters corresponding to the at least one elected activity, action or task into the subject's DBS device such that the subject's DBS device operates under the selected group of DBS parameters, measuring and quantifying motor symptoms of the subject based at least in part on the signal from the at least one physiological or movement sensor(s) while the subject performs the at least one elected activity, action or task, entering data corresponding to the subject's measured and quantified motor symptoms into an algorithm, providing, with the algorithm, a second level of DBS parameters based at least in part on the data entered into the algorithm, the second level of DBS parameters maximizing the subject's ability to perform the at least one elected activity, action or task, transmitting data corresponding to the subject's measured and quantified motor symptoms and the second level of DBS parameters to a clinician, physician or technician at a remote location, having the clinician, physician or technician approve or edit the second level of DBS parameters provided by the algorithm, transmitting the approved or edited second level of DBS parameters to the subject's DBS device, and entering the second level of DBS parameters into the subject's DBS device such that the subject's DBS device operates under the second level of DBS parameters while the subject performs the at least one elected activity, action or task.

Still even another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of providing a movement disorder diagnostic device to a subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, and a processor comprising an algorithm, instructing the subject to perform at least one movement disorder test(s) while the subject is undergoing DBS therapy or is under the effects of DBS therapy, measuring and quantifying motor symptoms of the subject based at least in part on the signal from the at least one physiological or movement sensor(s) during the at least one movement disorder test(s), entering the data corresponding to the subject's measured and quantified motor symptoms into an algorithm, providing, with the algorithm, a second level of DBS parameters based at least in part on the data entered into the algorithm, transmitting data corresponding to the subject's measured and quantified motor symptoms and the second level of DBS parameters to a clinician, physician or technician at a remote location, having the clinician, physician or technician approve or edit the second level of DBS parameters provided by the algorithm, transmitting the approved or edited second level of DBS parameters to the subject's DBS device, and automatically entering the second level of DBS parameters into the subject's DBS device such that the subject's DBS device operates under the second level of DBS parameters.

Even still another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of having a subject wear a movement disorder diagnostic device, the subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, and the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, identifying or determining what activity, movement or motion the subject is performing, identifying or determining motor symptoms of a movement disorder or side effects of a treatment the subject is undergoing that the subject is suffering or has suffered based at least in part on the signal(s) from the at least one physiological or movement sensor and at least in part on the identified or determined activity, movement or motion, measuring and quantifying the identified or determined motor symptoms or side effects with a processor based at least in part on the signal(s) from the at least one physiological or movement sensor while the subject is wearing the diagnostic device, entering data corresponding to the measured and quantified motor symptoms or side effects into the processor, the processor comprising an algorithm, calculating, with the processor, a second level of DBS parameters based at least in part on the data entered into the processor, transmitting the second level of DBS parameters to the subject's DBS device, and entering the second level of DBS parameters into the subject's DBS device such that the subject's DBS device operates under the calculated second level of DBS parameters.

Even still yet another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of having a subject wear a movement disorder diagnostic device, the subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, and the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, identifying or determining what activity, movement or motion the subject is performing, identifying or determining motor symptoms of a movement disorder or side effects of a treatment the subject is undergoing that the subject is suffering or has suffered based at least in part on the signal(s) from the at least one physiological or movement sensor and at least in part on the identified determined activity, movement or motion, measuring and quantifying the identified or determined motor symptoms or side effects with a processor based at least in part on the signal(s) from the at least one physiological or movement sensor while the subject is wearing the diagnostic device, entering data corresponding to the measured and quantified motor symptoms or side effects into the processor, the processor comprising an algorithm, calculating, with the processor, at least two optional groups of DBS parameters based at least in part on the data entered into the processor, each optional group addressing a separate identified or detected symptom or side effect, having the subject select one optional group of DBS parameters based on the subject's activity, movement or motion, transmitting the selected optional group of DBS parameters to the subject's DBS device, and entering the selected optional group of DBS parameters into the subject's DBS device such that the subject's DBS device operates under the selected optional group of DBS parameters.

Yet even still another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of having a subject wear a movement disorder diagnostic device, the subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, and the movement disorder diagnostic device comprising at least two physiological or movement sensors, each having a signal, identifying or determining what activity, movement or motion the subject is performing, identifying or determining motor symptoms of a movement disorder or side effects of a treatment that the subject is undergoing that the subject is suffering or has suffered based at least in part on the signals from the at least two physiological or movement sensors, and at least in part on the identified or determined activity, movement or motion, measuring and quantifying the identified or determined motor symptoms or side effects with a processor based at least in part on the signals from the at least two physiological or movement sensors and the identified or determined symptoms or side effects while the subject is wearing the diagnostic device, entering data corresponding to the measured and quantified motor symptoms or side effects into the processor, the processor comprising an algorithm, calculating, with the processor, a second level of DBS parameters based at least in part on the data entered into the algorithm, transmitting the second level of DBS parameters to the subject's DBS device, and entering the second level of DBS parameters into the subject's DBS device such that the subject's DBS device operates under the second level of DBS parameters, wherein the second level of DBS parameters is an optimization of parameters addressing or treating to some degree each of the identified or determined symptoms or side effects based on the identified or determined activity, movement or motion.

Still even yet another embodiment of the present invention includes method of adjusting a treatment or therapy device to reduce a subject's symptoms of a movement disorder or side effects of treatment or therapy of a subject's movement disorder provided by the treatment or therapy device, the method comprising steps of: measuring a subject's brain activity during a first time period; simultaneously measuring during the first time period the subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder; changing parameters of the subject's treatment or therapy; measuring the subject's brain activity during a second time period; simultaneously measuring during the second time period the subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder; determining the brain activity that minimizes the measured symptoms or side effects by correlating the measured symptoms or side effects with the simultaneously measured brain activity to map the part(s) of the subject's brain responsible for the symptoms or side effects; training a tuning algorithm comprised in a processor based on the determined brain activity that minimizes the measured symptoms or side effects; calculating, with the tuning algorithm, a suggested set of treatment or therapy parameters based at least in part on the measured brain activity and measured symptoms or side effects, the suggested set of parameters adapted to be reviewed and approved by a clinician, physician, or technician; and entering approved treatment or therapy parameters into the treatment or therapy device such that the treatment or therapy device provides treatment or therapy to the subject based on the approved parameters.

Even yet still another embodiment of the present invention includes a method of adjusting a treatment or therapy device to reduce a subject's symptoms of a movement disorder or side effects of treatment or therapy of a subject's movement disorder provided by the treatment or therapy device, the method comprising steps of: measuring a subject's brain activity during a first time period; simultaneously measuring during the first time period the subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder; changing parameters of the subject's treatment or therapy; measuring the subject's brain activity during a second time period; simultaneously measuring during the second time period the subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder; determining the brain activity that minimizes the measured symptoms or side effects by correlating the measured symptoms or side effects with the simultaneously measured brain activity to map the part(s) of the subject's brain responsible for the symptoms or side effects; training a tuning algorithm comprised in a processor based on the determined brain activity that minimizes the measured symptoms or side effects; calculating, with the tuning algorithm, a suggested set of treatment or therapy parameters based at least in part on the measured brain activity and measured symptoms or side effects; and entering the suggested set of treatment or therapy parameters into the treatment or therapy device such that the treatment or therapy device provides treatment or therapy to the subject based on the suggested set of parameters.

Yet still even another embodiment of the present invention includes a method of adjusting a treatment or therapy device to reduce a subject's symptoms of a movement disorder or side effects of treatment or therapy of a subject's movement disorder provided by the treatment or therapy device, the method comprising steps of: measuring a subject's brain activity during a first time period; simultaneously measuring during the first time period the subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder; changing parameters of the subject's treatment or therapy; measuring the subject's brain activity during a second time period; simultaneously measuring during the second time period the subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder; determining the brain activity that minimizes the measured symptoms or side effects by correlating the measured symptoms or side effects with the simultaneously measured brain activity to map the part(s) of the subject's brain responsible for the symptoms or side effects; training a tuning algorithm comprised in a processor based on the determined brain activity that minimizes the measured symptoms or side effects; measuring the subject's brain activity during a third time period; calculating, with the tuning algorithm, a suggested set of treatment or therapy parameters based at least in part on the brain activity measured during the third time period; and entering the suggested set of treatment or therapy parameters into the treatment or therapy device such that the treatment or therapy device provides treatment or therapy to the subject based on the suggested set of parameters.

Even yet still another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of: having a subject wear a movement disorder diagnostic device, the subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, the DBS device comprising at least one dual-mode DBS lead with at least one or a plurality of contacts adapted to provide electrical stimulation and to acquire physiological signals, each contact having a signal; measuring the subject's external body motion with the at least one physiological or movement sensor; measuring the subject's brain activity substantially simultaneously with measuring the external body motion; identifying or determining motor symptoms of a movement disorder or side effects of a treatment the subject is undergoing that the subject is suffering or has suffered based at least in part on the signal(s) from the at least one physiological or movement sensor and at least in part on the signals from the plurality of DBS lead contacts; measuring and quantifying the identified or determined motor symptoms or side effects with a processor comprising a scoring algorithm based at least in part on the signal(s) from the at least one physiological or movement sensor and at least in part on the signals from the DBS lead contacts while the subject is wearing the diagnostic device; entering data corresponding to the measured and quantified motor symptoms or side effects into the processor, the processor comprising a tuning algorithm; calculating, with the tuning algorithm, a second level of DBS parameters based at least in part on the data entered into the processor; transmitting the second level of DBS parameters to the subject's DBS device; and entering the second level of DBS parameters into the subject's DBS device such that the subject's DBS device operates under the calculated second level of DBS parameters.

Yet still even another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of: having a subject wear a movement disorder diagnostic device, the subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, the DBS device comprising at least one dual-mode DBS lead with a plurality of contacts adapted to provide electrical stimulation and to acquire physiological signals, each contact having a signal; identifying or determining what activity, movement or motion the subject is performing; measuring the subject's external body motion with the at least one physiological or movement sensor; measuring the subject's brain activity substantially simultaneously with measuring the external body motion; identifying or determining motor symptoms of a movement disorder or side effects of a treatment the subject is undergoing that the subject is suffering or has suffered based at least in part on the signal(s) from the at least one physiological or movement sensor, at least in art based on the signals from the plurality of DBS lead contacts, and at least in part on the identified determined activity, movement or motion; measuring and quantifying the identified or determined motor symptoms or side effects with a processor comprising a scoring algorithm based at least in part on the signal(s) from the at least one physiological or movement sensor and at least in part on the signals from the DBS lead contacts while the subject is wearing the diagnostic device; entering data corresponding to the measured and quantified motor symptoms or side effects into the processor, the processor comprising a tuning algorithm; calculating, with the tuning algorithm, a second level of DBS parameters based at least in part on the data entered into the processor; transmitting the second level of DBS parameters to the subject's DBS device; and entering the second level of DBS parameters into the subject's DBS device such that the subject's DBS device operates under the calculated second level of DBS parameters.

Still even yet another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of: providing to the subject a deep brain stimulation (DBS) device with a first level of DBS parameters, the DBS device comprising at least one dual-mode DBS lead with a plurality of contacts adapted to provide electrical stimulation and to acquire physiological signals, each contact having a signal; measuring the subject's brain activity substantially simultaneously with measuring the external body motion; identifying or determining motor symptoms of a movement disorder or side effects of a treatment the subject is undergoing that the subject is suffering or has suffered based at least in part on the signals from the plurality of DBS lead contacts; measuring and quantifying the identified or determined motor symptoms or side effects with a processor comprising a scoring algorithm based at least in part on the signals from the DBS lead contacts while the subject is wearing the diagnostic device; entering data corresponding to the measured and quantified motor symptoms or side effects into the processor, the processor comprising a tuning algorithm; calculating, with the tuning algorithm, a second level of DBS parameters based at least in part on the data entered into the processor; transmitting the second level of DBS parameters to the subject's DBS device; and entering the second level of DBS parameters into the subject's DBS device such that the subject's DBS device operates under the calculated second level of DBS parameters.

Still another embodiment of the present invention includes a method of reducing a subject's symptoms of a movement disorder or side effects of treatment of a subject's movement disorder comprising steps of: measuring a subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder during a first time period; simultaneously measuring a subject's brain activity during the first time period; obtaining a first symptom brain activity map by correlating the first time period measured symptoms or side effects with the first time period measured brain activity; providing treatment to the subject; measuring a subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder during a second time period; simultaneously measuring a subject's brain activity during the second time period; obtaining a second symptom brain activity map by correlating the second time period measured symptoms or side effects with the second time period measured brain activity; changing parameters of the treatment provided to the subject based at least in part on the differences between the first and second brain activity maps to reduce the measured symptoms or side effects.

Yet another embodiment of the present invention includes a method of tuning a movement disorder therapy system comprising steps of: providing a movement disorder diagnostic device (M3D) to a subject having a deep brain stimulation (DBS) device with treatment parameters, the M3D comprising at least one external physiological or movement sensor having a signal, and at least one brain activity sensor having a signal; simultaneously measuring with the M3D, during a first time period, brain activity and symptoms of the subject's movement disorder or side effects of treatment of the movement disorder; changing the first level of DBS parameters based at least in part on the measured brain activity and symptoms or side effects; simultaneously measuring with the M3D, during a second time period, brain activity and symptoms of the subject's movement disorder or side effects of treatment of the movement disorder; determining brain activity that minimizes the measured symptoms or side effects; training a tuning algorithm based at least in part on the determination of brain activity that minimizes the measured symptoms or side effects; and adjusting the treatment parameters of the subject's DBS device to achieve the brain activity determined to minimize the measured symptoms or side effects.

It is to be understood that both the foregoing general description and the following detailed description are merely exemplary of the invention, and are intended to provide an overview or framework for understanding the nature and character of the invention as it is claimed. The accompanying drawings are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification. The drawings illustrate various embodiments of the invention and together with the description serve to explain the principles and operation of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Schematic view of a subject undergoing post-surgical DBS adjustment with one embodiment of the invention involving automated instruction of the subject and electronic transmission between the system and the subject's DBS device.

FIG. 2. Schematic view of a subject undergoing post-surgical DBS adjustment with another embodiment of the invention involving electronic transmission between the system and the subject's DBS device.

FIG. 3. Flow diagram of a parameter adjustment suggestion algorithm in some embodiments of the present invention.

FIG. 4. Flow diagram of an artificial neural network of the parameter adjustment suggestion algorithm in some embodiments of the present invention.

FIG. 5. Graphic depiction of display pages displaying test results and scores, one embodiment of a tuning map, as well as one embodiment of a parameter input screen.

FIG. 6. Graphic depiction of one embodiment of tuning maps used to display test results and symptom severity objectively measured by the system and displayed as a scatter plot of symptom severity scores.

FIG. 7. Illustration of the subject screening process to determine if DBS is a viable option for a particular subject.

FIG. 8. Illustration of various embodiments of the DBS parameter programming/tuning process.

FIG. 9A-B. Illustration depicting the difference between different DBS lead configurations including (A) monopolar DBS lead configurations which do not allow shaping of the electrical stimulation field, and (B) bipolar DBS lead configurations which are able to shape the electrical stimulation field to avoid activating a side effect region of the brain.

FIG. 10. One embodiment of an interface device allowing a user to select an activity to perform, and which can change the subject's therapy parameters or settings based on the selected activity.

FIG. 11. Flow chart describing a method embodiment wherein the system identifies an activity the subject is performing and any motor symptoms or side effects the subject is experiencing, then measures and quantifies the symptoms or side effects, calculates a second level of therapy parameters, and enters the updated parameters into the subjects therapy device.

FIG. 12. Flow chart describing a method embodiment wherein the system identifies an activity the subject is performing and any motor symptoms or side effects the subject is experiencing, then measures and quantifies the symptoms or side effects, calculates at least two optional groups of therapy parameters, at least one of which is selected by the subject to be used, and the system enters the selected parameters into the subjects therapy device.

FIG. 13. Flow chart describing a method embodiment wherein the system identifies an activity the subject is performing and any motor symptoms or side effects the subject is experiencing, then measures and quantifies the symptoms or side effects, calculates a second level of therapy parameters, and enters the updated parameters into the subjects therapy device, where the updated parameters are optimized to address each of the identified activity and symptoms and side effects.

FIG. 14. Flow chart describing one programming option embodiment of the present invention wherein an algorithm(s) provides a recommended set of parameters or settings to be reviewed and approved by a clinician at a remote location before those parameters or settings are programmed into the subject's therapy device.

FIG. 15. Flow chart depicting a start-to-finish description of the process from the subject exhibiting movement disorder symptoms through treatment and therapy of those movement disorder symptoms, and where the subject's therapy device is programmed newly determined therapy parameters or settings.

FIG. 16. Flow chart of one exemplary embodiment of an intelligent tuning algorithm for determining or selecting a second set or group of therapy parameters or settings based on measured and quantified movement data and/or movement disorder and various constraints.

FIG. 17. Flow chart of one exemplary embodiment of an integrated tuning algorithm for determining or selecting a second set or group of therapy parameters or settings based on measured and quantified movement data and/or movement disorder and various constraints where a clinician at a remote location determines the second group which is then transmitted to the subject's device.

FIG. 18. Flow chart of one exemplary embodiment of an integrated tuning algorithm for determining or selecting a second set or group of therapy parameters or settings based on measured and quantified movement data and/or movement disorder and various constraints where a clinician at a remote location determines at least two optional groups, only one of which is selected for use, and the selected group is then transmitted to the subject's device.

FIG. 19. Flow chart of one exemplary embodiment of an integrated tuning algorithm for determining or selecting a second set or group of therapy parameters or settings based on measured and quantified movement data and/or movement disorder and various constraints where a clinician at a remote location determines the second group which is then transmitted to the subject's device and the group is entered automatically into the subject's device no direct interaction between the clinician and the subject's therapy device.

FIG. 20. Flow chart of one exemplary embodiment of an integrated tuning algorithm for determining or selecting a second set or group of therapy parameters or settings based on measured and quantified movement data and/or movement disorder and various constraints where a clinician at a remote location determines the second group which is then transmitted to the subject's device, and where the therapy parameters or settings and/or the quantified motor symptom data is transmitted to a database for storage and/or access by a clinician.

FIG. 21. Image depicting a typical human brain labeled with the various lobes and cortexes known and identified.

FIG. 22. Image depicting a traditional map of the human brain motor cortex correlated with known regions of body controlled by particular portions of the motor cortex.

FIG. 23. Flow chart describing a method embodiment wherein the system identifies an activity the subject is performing and any motor symptoms or side effects the subject is experiencing and correlates those movements and symptoms to measured brain activity, then measures and quantifies the symptoms or side effects, calculates a second level of therapy parameters, and enters the updated parameters into the subject's therapy device.

FIG. 24. Flow chart describing a method embodiment wherein the system identifies an activity, movement or motion the subject is performing and any motor symptoms or side effects the subject is experiencing based on detected and/or measured brain activity, then measures and quantifies the symptoms or side effects, calculates a second level of therapy parameters, and enters the updated parameters into the subject's therapy device.

FIG. 25. Flow chart depicting an embodiment of the testing and measurement process for obtaining data related to a subject's external body movement or motion and/or physiological data and correlating such with measured brain activity to map the areas of the subject's brain related to particular types of motion.

FIGS. 26A-B. Images depicting mapped brain activity related to particular types of body motion, specifically for A) arm movements and B) leg movements.

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to methods for semi-automatically and automatically adjusting, or tuning, treatment parameters in movement disorder therapy systems either in a location separate from the patients (remotely) or at the patient's location. Semi-automatic adjustment includes providing the clinician, physician or technician with objective, quantitative or semi-quantitative data or measurements related to a subject's movement disorder symptoms, determining desired parameters, and then entering those parameters either manually, semi-automatically or automatically into the subject's device. Semi-automatic and automatic adjustment includes providing objective, quantitative or semi-quantitative data or measurements related to a subject's movement disorder symptoms to an algorithm, determining desired parameters using the algorithm, and then entering those parameters either semi-automatically (i.e., allowing clinician, physician or technician to accept the recommended adjustment to the parameters or settings) or automatically (i.e., parameters or settings are approved and automatically transmitted and entered) into the subject's therapy device. The present invention further relates to a system for screening patients to determine if they are viable candidates for certain therapy modalities. The present invention still preferably further provides methods of quantifying movement disorders for the treatment of patients who exhibit symptoms of such movement disorders including, but not limited to, Parkinson's disease and Parkinsonism, Dystonia, Chorea, and Huntington's disease, Ataxia, Tremor and Essential Tremor, Tourette syndrome, stroke, and the like. The present invention yet further relates to methods of automatically and intelligently tuning a therapy device using objective quantified movement disorder symptom data acquired by a movement disorder diagnostic device with the therapy setting or parameters to be provided to the subject via his or her therapy device.

The movement disorder diagnostic device, systems and/or methods of the various embodiments of the present invention preferably are used to screen patients, analyze, score, and treat various disorders, and especially movement disorders and mental health disorders. Movement disorders for purposes of this application include but are not limited to Parkinson's disease and Parkinsonism, Dystonia, Chorea, and Huntington's disease, Ataxia, Tremor and Essential Tremor, Tourette syndrome, stroke, and the like. Mental health disorders include, but are not limited to major depression, bipolar disorder, obsessive compulsive disorder, and antisocial disorders. Some of the treatments used for these disorders involve pharmaceutical interventions, fetal cell transplants, surgery, or deep brain stimulation. The efficacy of an intervention is often judged by the intervention's ability to alleviate subject symptoms and improve subject quality of life. The subject on which the system or method is used is a human or another form of animal.

The movement disorder diagnostic device the various embodiments of the present invention are preferably portable. By portable it is meant among other things that the device is capable of being transported relatively easily. Relative ease in transport means that the device can be carried by a single person, generally in a carrying case to the point of use or application. Additionally, relative ease in transport means that the device is easily worn, carried by or attached to a subject. Furthermore the device preferably should be relatively light-weight. By relatively lightweight, preferably the device weighs less than about 3 lbs., more preferably less than about 2 lbs., even more preferably less than about 1 lb., still more preferably less than about 0.5 lbs., still preferably less than about 2 ounces and most preferably less than 0.5 ounces. By being lightweight and further compact, the device should gain greater acceptance for use by the subject. The system for measuring and calculating the severity of the symptoms including external computers preferably weighs less than about 15 lbs., more preferably less than about 10 lbs., still more preferably less than about 5 lbs., even more preferably less than about 2 lbs., and most preferably less than 0.5 lbs. This system more preferably can fit in a reasonably sized carrying case so the patient or their caregiver can easily transport the system.

Another advantage of the systems and methods of the present invention is optionally the ability to determine or calculate the severity of a subject's symptoms in real time. Throughout this disclosure, by real time it is meant that within 30 minutes of sensing and measurement the severity of a subject's symptoms can be calculated or determined. Real time, more preferably means that the subject's symptoms can be calculated or determined in less than about 30 seconds, more preferably in less than about 1 second, even more preferably in less than about 0.1 seconds, and most preferably in less than about 0.01 seconds.

The devices of the various embodiments of the present invention can form part of a system for use by a physician, veterinarian, technician or clinician for analysis or evaluation of a subject's movement disorder; for pharmaceutical research, for adjustment of neurostimulation therapy such as for example deep brain stimulation (DBS) or spinal cord stimulation (SCS), or for delivery of pharmaceutical compounds. Other elements of this system may include but are not limited to receivers, routers, communication devices, processors, displays, drug delivery devices and the like, some of which are described further in various embodiments described in more detail below.

The preferable movement disorder diagnostic device, described in greater detail below, worn, carried by or attached to the subject, contains various physiological or movement sensor(s) used to measure the subject's external body motion and/or other physiological signals from the subject's body. The movement disorder diagnostic device may temporarily store the subject's movement or physiological data in onboard memory and/or transmit this data to an external device. In some embodiments, the movement disorder diagnostic device may directly transmit the data to a centralized database, to multiple databases at the same or multiple locations, or to a cloud-based database where the data can be stored and accessed essentially immediately by authorized users who can analyze and/or further process the data, use it to diagnose or assess the subject's symptoms or disorders, or the like. Additionally, or alternatively, the movement disorder diagnostic device can transmit the movement or physiological data to an external computer device, or directly to a remote location for access by a clinician, physician or technician. Transmission to a remote location preferably may include transmission directly to such a computer device at said remote location, or may involve a user (such as a clinician, physician or technician) at the remote location accessing the data or information through the database or databases as described. The computer device, though called a tablet herein, is understood to be any type of device known to those skilled in the art usable for the intended purpose(s) or function(s), including, but not limited to, desktop computers, laptop computers, tablet computers, personal digital assistants (PDAs, “smart” cellular telephones, and the like). The computer device, or tablet, may be provided as part of the present invention's system, but in many embodiments the movement disorder diagnostic device is designed to work with and communicate with such devices of any third-party manufacturer or provider who provides such devices for the intended function or purpose of the present invention. In such cases, a software installation providing the user interface, tuning map capabilities, diagnostic and analysis tools, and the like would simply be installed on the third-party computer device or tablet as software or an application (or “app”), or the interaction with the user(s) can be web based through a web portal. The tuning map is one example of a tool that allows the clinician, physician or technician to review and/or determine the next, or preferably best (optimized) therapeutic settings or parameters for the subject's therapy device, such as a DBS device. In the present invention, tuning maps are used primarily as a tool for review and analysis of the automatically, intelligently generated parameters or settings provided by a tuning algorithm, and not for full-time or regular interaction and programming by a clinician, physician or technician. The tuning maps are described in greater detail in U.S. Pat. Nos. 9,393,418 and 9,662,502, both of which are herein incorporated by reference. Once the next therapy parameters or settings have been determined, a programmer device or unit can be used to communicate directly with the therapy device. Again, the programmer device or unit may be integrated into the subject-worn diagnostic device, a separate unit in and of itself, or may be part of the tablet or computer, and automatically communicates the parameters or settings to the programmer device or unit or directly to the subject's therapy device.

As noted, various embodiments of the present invention may include a sensor for measuring a subject's external body motion. The invention may also include at least one sensor for indirectly measuring movement metrics. Many types of sensors are known by those skilled in the art for measuring external body motion or providing physiological signals through which body movement information may be derived. External body motion sensors include but are not limited to accelerometers, gyroscopes, magnetometers, resistive bend sensors, combinations thereof, and the like. Preferably, a combination using at least an accelerometer and gyroscope is used. Sensors through which body movement information may be derived include, but are not limited to, electromyogram (EMG), electrooculogram (EOG), electroencephalogram (EEG), electrocardiogram (EKG), or other physiological signals which can directly or indirectly measure movement metrics in the subject may be included if such sensors and signals may be used to sense, detect, measure, and/or quantify the subject's external body motion, or related aspects.

In embodiments where a gyroscope is a sensor of the present invention, the gyroscope functions on the principle of the Coriolis Effect and a capacitive-based sensing system. Rotation of the sensor causes a shift in response of an oscillating silicon structure resulting in a change in capacitance. A typical application specific integrated circuit (ASIC), manufactured using a standard complementary metal oxide semiconductor (CMOS) manufacturing process, detects and transforms changes in capacitance into an analog output voltage, which is proportional to angular rate. The sensor element design utilizes differential capacitors and symmetry to significantly reduce errors from acceleration and off-axis rotations.

In embodiments where an accelerometer is a sensor of the present invention, it may optionally be a dual axis acceleration measurement system on a single monolithic integrated circuit (IC). Such embodiments may contain a polysilicon surface-micromachined sensor and signal conditioning circuitry to implement open-loop acceleration measurement architecture. For each axis an output circuit converts the analog signal to a duty cycle modulated (DCM) digital signal that can be decoded with a counter/timer port on a microprocessor. The dual axis accelerometer is capable of measuring both positive and negative accelerations. The sensor may be a surface micromachined polysilicon structure built on top of the silicon wafer. Polysilicon springs suspend the structure over the surface of the wafer and provide a resistance against acceleration forces. Deflection of the structure is measured using a differential capacitor that consists of independent fixed plates and central plates attached to the moving mass. The fixed plates are driven by 180-degree out of phase square waves. Acceleration will deflect the beam and unbalance the differential capacitor, resulting in an output square wave whose amplitude is proportional to acceleration. Phase sensitive demodulation techniques are then used to rectify the signal and determine the direction of the acceleration. The output of the demodulator drives a duty cycle modulator (DCM) stage through a 32 kOhm resistor. At this point a pin is available on each channel to allow the user to set the signal bandwidth of the device by adding a capacitor. This filtering improves measurement resolution and helps prevent aliasing. After being low-pass filtered, the analog signal is converted to a duty cycle modulated signal by the DCM stage. A single resistor sets the period for a complete cycle (T2). A 0 g acceleration produces a nominally 50% duty cycle. The acceleration signal can be determined by measuring the length of the T1 and T2 pulses with a counter/timer or with a polling loop using a low cost microcontroller.

In preferred embodiments, a single sensor unit comprising at least an accelerometer and a gyroscope may be used. More preferably, a single chip containing both a 3-axis accelerometer and a 3-axis gyroscope (e.g., Invensense MPU-6000), may be used. The sensor unit preferably not only comprises at least an accelerometer and a gyroscope, but also allows for integration of other sensors external to the sensor unit. Preferably, the accelerometer and gyroscope are each three-axis sensors capable of measuring their respective movements (acceleration and orientation) in each of the three dimensions of movement (X, Y and Z). Each of the accelerometer and gyroscope may output a separate signal for their respective measurements in each axis, and these signals are all converted from analog to digital by a bank of analog-to-digital converters (ADC). The separate ADCs for each axis of the accelerometer and gyroscope allow for simultaneous sampling of each sensor and eliminate the need for an external multiplexer. Preferably the sensor unit as a whole, and the accelerometer and gyroscope in particular are capable of operation with low power consumption. Preferably, the accelerometer and gyroscope are user-programmable such that the user may define an operating range in which the sensors will work (e.g., the accelerometer may be programmed to operate from as low as ±2 g to as high as ±16 g, and the gyroscope from as low as ±250 degrees/second to as high as ±2000 degrees/second). Some embodiments may include other sensors integrated into the sensor unit as well, for example, a temperature sensor, which may be used to monitor the temperature of the sensor unit and ensure it is operating properly and under safe conditions.

The sensor unit further preferably comprises a digital motion processor (DMP), which may perform some preprocessing or processing of the sensor signals using motion-related algorithms. The digital motion processor at least preprocesses and/or processes the accelerometer and gyroscope signals to begin the analysis of the signals and to decrease the processing load on the external processor. Many embodiments may include external or additional sensors that are not housed within the sensor unit, but whose signals are transmitted to the sensor unit for integration with the accelerometer and gyroscope signals for further transmission to external components such as a processor. Such external or additional sensors may include, but are not limited to, force sensors, magnetometers, pressure sensors, bend sensors, combinations thereof, and the like. These external or additional sensors communicate with the sensor unit by means of an auxiliary communications interface. The digital motion processor can integrate the signal(s) from these external or additional sensors along with the accelerometer and gyroscope signals and perform preprocessing or processing of all of the signals together, thus further streamlining the data acquisition process and reducing the workload of the external processor (not shown).

In many embodiments, the movement disorder diagnostic device comprises a kinetic sensor board (or subject worn external sensor). The kinetic sensor board is preferably configured with at least an accelerometer and a gyroscope for quantifying the subject's motion. In some embodiments, the kinetic sensor board comprises at least three gyroscopes and three orthogonal accelerometers, but in more preferable embodiments the three of each sensor are replaced by at least one 3-axis accelerometer and at least one 3-axis gyroscope. The kinetic sensor board also includes a microprocessor and a power interface section.

In many embodiments, the electrical components of the movement disorder diagnostic device further include a power receiver. The power receiver is the component, which receives the electrical charge from the external power source (not shown). The external power source can be any device for supplying power to the movement disorder diagnostic device. In some embodiments, the external power source may be a docking station to which the movement disorder diagnostic device can be connected, attached, docked, or placed into whereby a physical or proximal connection is made between the docking station and the movement disorder diagnostic device thus allowing power to be transferred via the physical or proximal connection. In other embodiments, the external power source may merely involve plugging the movement disorder diagnostic device into a traditional power outlet. In still other embodiments, the external power source may be an inductive charging mat or pad onto which the movement disorder diagnostic device is placed and power may be inductively transferred between induction coils in the charging mat or pad and the inductive coils in the power receiver of the movement disorder diagnostic device, as described herein. As the power receiver, which may be wireless or wired depending on the embodiment, receives power, it transfers said power to a power manager which controls and directs where the incoming power is delivered. If the movement disorder diagnostic device is not being presently used to measure a subject's body movements and is instead being charged, then the power manager directs the incoming power to the device's battery for charging. It might be possible and optional, though not necessarily preferred, for some embodiments to allow charging while the unit is being used to measure a subject's body motions, in which case the power manager would direct the incoming power to either the battery or to the micro-controller for powering the device's operation for testing. However, it is more preferable for the device, during operation for testing, to be untethered and not in charging mode, and thus the battery would provide power to the unit for usage and testing purposes. The micro-controller or microprocessor is the internal processing unit that directs the other components to function. Thus, the micro-controller or microprocessor directs the power manager on where to direct the power it is receiving from either the power receiver or the battery. An electronic clock operates as commonly known in the art to control synchronization and operation of the device to maximize efficiency of power usage. The radio of the device controls and carries out communications between the device components, and between the movement disorder diagnostic device and external devices (not shown). The radio receives power directly from the power manager. As described herein, the radio may be a Bluetooth® communications device to provide wireless communications with external components such as computers or processors, data acquisition circuitry, internet or cloud-based memory banks or databases, and the like, as well as internal components such as the internal movement disorder diagnostic device memory, microprocessor, and the like. Both internal (between electrical components of the subject-worn sensor device) and external (between the subject-worn sensor device and external components or devices) communications may also be transmitted through wireless, wired, or a combination of both methods. The micro-controller comprises algorithms and protocols for coordinating the operation of at least these internal electrical components, and in some embodiments also for preprocessing or processing sensor data.

The movement disorder diagnostic device of the present invention further preferably comprises a transceiver module, or command module. Preferably the sensor unit and transceiver/command module are enclosed in the same housing constituting a single unit, though they may be separate units. The transceiver module includes communications electronics, such as a Bluetooth® radio (e.g., BlueGiga WT12) to provide wireless communications with the patient PC, on board memory, a microprocessor (e.g., Silicon Labs C8051F930), and a battery power supply (e.g., Kokam Lithium Power battery) that supplies power to both the transceiver module and one or more sensor modules. The transceiver module may also include a USB port to provide battery recharging and serial communications with the patient PC. The transceiver module may also include a push button input.

In many embodiments, the transceiver/command module contains one or more electronic components such as a microprocessor for detecting both the signals from the gyroscopes and accelerometers. Optionally, the one or more electronic components also filter the kinetic motion signals, and more preferably convert these signals, which are in an analog form into a digital signal for transmission to a remote receiving unit, computer or other similar device. Though, more preferably, the device uses the herein described 3-axis accelerometer and 3-axis gyroscope chip which comprises ADC circuitry and thus outputs a digital signal. The one or more electronic components are attached to the subject as part of the movement disorder diagnostic device. Further preferably, the one or more electronic components can receive a signal from the remote receiving unit or other remote transmitters. The one or more electronic components may include circuitry for but are not limited to, for example, electrode amplifiers, signal filters, analog to digital converter, Bluetooth® radio or other receiver, transmitter or transceiver components, a DC power source and combinations thereof. The one or more electronic components may comprise one processing chip, multiple chips, single function components or combinations thereof, which can perform all of the necessary functions of detecting a kinetic or physiological signal from the electrode, storing that data to memory, uploading data to a computer through a serial link, transmitting a signal corresponding to a kinetic or physiological signal to a receiving unit and optionally receiving a signal from a remote transmitter. These one or more electronic components can be assembled on a printed circuit board or by any other devices or methods known to those skilled in the art including but not limited to an ASIC chip. Preferably, the one or more electronic components can be assembled on a printed circuit board or by other means so its imprint covers an area less than 4 in², more preferably less than 2 in², even more preferably less than 1 in², still even more preferably less than 0.5 in², and most preferably less than 0.25 in².

Preferably, the circuitry of the one or more electronic components is appropriately modified so as to function with any suitable miniature DC power source. More preferably, the DC power source is a battery. The most preferred battery of the present invention is lithium powered batteries. Lithium ion batteries offer high specific energy (the number of given hours for a specific weight), which is preferable. Additionally, these commercially available batteries are readily available and inexpensive. Other types of batteries include but are not limited to primary and secondary batteries. Primary batteries are not rechargeable since the chemical reaction that produces the electricity is not reversible. Primary batteries include lithium primary batteries (e.g., lithium/thionyl chloride, lithium/manganese dioxide, lithium/carbon monofluoride, lithium/copper oxide, lithium/iodine, lithium/silver vanadium oxide and others), alkaline primary batteries, zinc-carbon, zinc chloride, magnesium/manganese dioxide, alkaline-manganese dioxide, mercuric oxide, silver oxide as well as zinc/air and others. Rechargeable (secondary) batteries include nickel-cadmium, nickel-zinc, nickel-metal hydride, rechargeable zinc/alkaline/manganese dioxide, lithium/polymer, lithium-ion and others.

In some preferred embodiments, the system is capable of inductive charging whereby an electromagnetic field is used to transfer energy from a charging mat or pad to the device. Preferably in such embodiments, the charging mat or pad comprises and induction coil that is used to create an alternating electromagnetic field. When the device, also comprising an induction coil, is placed on the charging mat or pad, the devices induction coil draws power from the electromagnetic field created by the charging mat's or pad's induction coil. The device's then converts this drawn power from electromagnetic field energy into electrical current and uses this electrical current to charge the device's battery.

Optionally, the data acquisition circuitry is designed with the goal of reducing size, lowering (or filtering) the noise, increasing the DC offset rejection and reducing the system's offset voltages. The data acquisition circuitry may be constrained by the requirements for extremely high input impedance, very low noise and rejection of very large DC offset and common-mode voltages, while measuring a very small signal of interest. Additional constraints arise from the need for a “brick-wall” style input protection against ESD and EMI. The exact parameters of the design, such as input impedance, gain and passband, can be adjusted at the time of manufacture to suit a specific application via a table of component values to achieve a specific full-scale range and passband.

Also optionally, a low-noise, lower power instrumentation amplifier is used. The inputs for this circuitry is guarded with preferably, external ESD/EMI protection, and very high-impedance passive filters to reject DC common-mode and normal-mode voltages. Still more preferably, the instrumentation amplifier gain can be adjusted from unity to approximately 100 to suit the requirements of a specific application. If additional gain is required, it preferably is provided in a second-order anti-bias filter, whose cutoff frequency can be adjusted to suit a specific application, with due regard to the sampling rate. Still yet more preferably, the reference input of the instrumentation amplifier is tightly controlled by a DC cancellation integrator servo that uses closed-loop control to cancel all DC offsets in the components in the analog signal chain to within a few analog-to digital converter (ADC) counts of perfection, to ensure long term stability of the zero reference.

Further optionally, where analog signals are acquired, such signals are converted to a digital form. This can be achieved with an electronic component or processing chip through the use of an ADC. More preferably, the ADC restricts resolution to 16-bits due to the ambient noise environment in such chips. Despite this constraint, the ADC remains the preferable method of choice for size-constrained applications such as with the present invention unless a custom data acquisition chip is used because the integration reduces the total chip count and significantly reduces the number of interconnects required on the printed circuit board.

Preferably, the circuitry of the sensor board comprises a digital section. More preferably, the heart of the digital section of the sensor board is a micro-controller or processor. The microcontroller or processor preferably contains sufficient data and program memory, as well as peripherals which allow the entire digital section to be neatly bundled into a single carefully programmed processing chip. Still more preferably, the onboard counter/timer sections are used to produce the data acquisition timer.

Preferably, the circuitry for the one or more electronic components is designed to provide for communication with external quality control test equipment prior to sale, and more preferably with automated final test equipment. In order to supply such capability without impacting the final size of the finished unit, one embodiment is to design a communications interface on a separate printed circuit board (PCB) using the SPI bus with an external UART and level-conversion circuitry to implement a standard serial interface for connection to a personal computer or some other form of test equipment. The physical connection to such a device requires significant PCB area, so preferably the physical connection is designed to keep the PCB at minimal imprint area. Optionally, the physical connection is designed with a break-off tab with fingers that mate with an edge connector. This allows all required final testing and calibration, including the programming of the processing chip memory, can be carried out through this connector, with test signals being applied to the analog inputs through the normal connections which remain accessible in the final unit. By using edge fingers on the production unit, and an edge connector in the production testing and calibration adapter, the system can be tested and calibrated without leaving any unnecessary electronic components or too large a PCB imprint area on the final unit. More preferably, no break-off tabs are required where a pogo-pin test pad design is used allowing the PCB to be tested without breaking apart.

Preferably, the circuitry for the one or more electronic components comprises nonvolatile, rewriteable memory for storing kinematic data, as well as RAM used to store operational data such as the pending mode (i.e., sleep or test mode), period and number of seconds to record data, daily alarm time, amount of time to collect data, and the like. Preferably, enough nonvolatile memory is included to record at least 8 hours of kinematic data, though preferably more. Alternatively, if the circuitry for the one or more electronic components doesn't comprise nonvolatile, rewriteable memory then an approach should be used to allow for reprogramming of the final parameters such as radio channelization and data acquisition and scaling. Without nonvolatile, rewriteable memory, the program memory can be programmed only once. Therefore one embodiment of the present invention involves selective programming of a specific area of the program memory without programming the entire memory in one operation. Preferably, this is accomplished by setting aside a specific area of program memory large enough to store several copies of the required parameters. Procedurally, this is accomplished by initially programming the circuitry for the one or more electronic components with default parameters appropriate for the testing and calibration. When the final parameters have been determined, the next area is programmed with these parameters. If the final testing and calibration reveals problems, or some other need arises to change the values, additional variations of the parameters may be programmed. The firmware of various embodiments of the present invention scans for the first blank configuration block and then uses the value from the preceding block as the operational parameters. This arrangement allows for reprogramming of the parameters up to several dozen times, with no size penalty for external EEPROM or other nonvolatile RAM. The circuitry for the one or more electronic components has provisions for in-circuit programming and verification of the program memory, and this is supported by the breakoff test connector, as well as the pop-pin test pad. The operational parameters can thus be changed up until the time at which the test connector is broken off just before shipping the final unit. Thus the manufacturability and size of the circuitry for the one or more electronic components is optimized. Most preferably, however, the system is designed to allow for over-the-air programming even once the circuit design has been completed and the circuit has been installed into the movement disorder diagnostic device. In such embodiments, the firmware contains a boot-loading program that, once turned on, looks for programming signals. Thus, such programming signals can be delivered and the device updated, even after manufacture and shipment to a clinic, or even when in the possession of a subject.

Preferably the circuitry of the one or more electronic components includes an RF transmitter and/or an RF receiver, or a RF transceiver. Still more preferably the circuitry of the one or more electronic components includes a Bluetooth® radio system requiring an average of about 42 mA of electrical current to operate. Another feature of the circuitry of the one or more electronic components preferably is an antenna. The antenna, preferably, is integrated in the rest of the circuitry. The antenna can be configured in a number of ways, for example as a single loop, dipole, dipole with termination impedance, logarithmic-periodic, dielectric, or strip conduction or reflector antenna. The antenna is designed to include but not be limited to the best combination of usable range, production efficiency and end-system usability. Preferably, the antenna consists of one or more conductive wires or strips, which are arranged in a pattern to maximize surface area. The large surface area will allow for lower transmission outputs for the data transmission. The large surface area will also be helpful in receiving high frequency energy from an external power source for storage. Optionally, the radio transmissions of the present invention may use frequency-selective antennas for separating the transmission and receiving bands, if a RF transmitter and receiver are used on the electrode patch, and polarization-sensitive antennas in connection with directional transmission. Polarization-sensitive antennas consist of, for example, thin metal strips arranged in parallel on an insulating carrier material. Such a structure is insensitive to or permeable to electromagnetic waves with vertical polarization; waves with parallel polarization are reflected or absorbed depending on the design. It is possible to obtain in this way, for example good cross polarization decoupling in connection with linear polarization. It is further possible to integrate the antenna into the frame of a processing chip or into one or more of the other electronic components, whereby the antenna is preferably realized by means of thin film technology. The antenna can serve to just transfer data or for both transferring data to and for receiving control data received from a computer device and/or receiving unit which can include but is not limited to a wireless relay, a computer or a processor system. Optionally, the antenna can also serve to receive high-frequency energy (for energy supply or supplement). In any scenario, only one antenna is required for transmitting data, receiving data and optionally receiving energy. Optionally, directional couples can be arranged on the transmitter outputs of the electrode patch and/or the computer device and/or receiving unit. The couplers being used to measure the radiated or reflected radio wave transmission output. Any damage to the antenna (or also any faulty adaptation) thus can be registered, because it is expressed by increased reflection values.

An additional feature of the present invention is an optional identification unit. By allocating identification codes—a patient code, the computer device and/or receiving unit is capable of receiving and transmitting data to several subjects, and for evaluating the data if the computer device and/or receiving unit is capable of doing so. This is realized in a way such that the identification unit has control logic, as well as a memory for storing the identification codes. The identification unit is preferably programmed by radio transmission of the control characters and of the respective identification code from the programming unit of the computer device and/or receiving unit to the patient worn unit. More preferably, the unit comprises switches as programming lockouts, particularly for preventing unintentional reprogramming.

In any RF link, errors are an unfortunate and unavoidable problem. Analog systems can often tolerate a certain level of error. Digital systems, however, while being inherently much more resistant to errors, also suffer a much greater impact when errors occur. Thus the present invention, when used as a digital system, preferably includes error control sub architecture. Preferably, the RF link of the present invention is digital. RF links can be one-way or two-way. One-way links are used to just transmit data. Two-way links are used for both sending and receiving data.

If the RF link is one-way error control, then this is preferably accomplished at two distinct levels, above and beyond the effort to establish a reliable radio link to minimize errors from the beginning. At the first level, there is the redundancy in the transmitted data. This redundancy is performed by adding extra data that can be used at the computer device and/or receiving unit or at some station to detect and correct any errors that occurred during transit across the airwaves. This mechanism known as Forward Error Correction (FEC) because the errors are corrected actively as the signal continues forward through the chain, rather than by going back to the transmitter and asking for retransmission. FEC systems include but are not limited to Hamming Code, Reed-Solomon and Golay codes. Preferably, a Hamming Code scheme is used. While the Hamming Code scheme is sometimes maligned as being outdated and underpowered, the implementation in certain embodiments of the present invention provides considerable robustness and extremely low computation and power burden for the error correction mechanism. FEC alone is sufficient to ensure that the vast majority of the data is transferred correctly across the radio link. Certain parts of the packet must be received correctly for the receiver to even begin accepting the packet, and the error correction mechanism in the computer device and/or receiving unit reports various signal quality parameters including the number of bit errors which are being corrected, so suspicious data packets can be readily identified and removed from the data stream.

Preferably, at a second, optional level, an additional line of defense is provided by residual error detection through the use of a cyclic redundancy check (CRC). The algorithm for this error detection is similar to that used for many years in disk drives, tape drives, and even deep-space communications, and is implemented by highly optimized firmware within the electrode patch processing circuitry. During transmission, the CRC is first applied to a data packet, and then the FEC data is added covering the data packet and CRC as well. During reception, the FEC data is first used to apply corrections to the data and/or CRC as needed, and the CRC is checked against the message. If no errors occurred, or the FEC mechanism was able to properly correct such errors as did occur, the CRC will check correctly against the message and the data will be accepted. If the data contains residual errors (which can only occur if the FEC mechanism was overwhelmed by the number of errors), the CRC will not match the packet and the data will be rejected. Because the radio link in this implementation is strictly one-way, rejected data is simply lost and there is no possibility of retransmission.

More preferably, the RF link utilizes a two-way (bi-directional) data transmission. By using a two-way data transmission the data safety is significantly increased. By transmitting redundant information in the data emitted by the electrodes, the computer device and/or receiving unit is capable of recognizing errors and request a renewed transmission of the data. In the presence of excessive transmission problems such as, for example transmission over excessively great distances, or due to obstacles absorbing the signals, the computer device and/or receiving unit is capable of controlling the data transmission, or to manipulate on its own the data. With control of data transmission it is also possible to control or re-set the parameters of the system, e.g., changing the transmission channel. This would be applicable for example if the signal transmitted is superimposed by other sources of interference then by changing the channel the computer device and/or receiving unit could secure a flawless and interference free transmission. Another example would be if the signal transmitted is too weak, the computer device and/or receiving unit can transmit a command to increase its transmitting power. Still another example would be the computer device and/or receiving unit to change the data format for the transmission, e.g., in order to increase the redundant information in the data flow. Increased redundancy allows transmission errors to be detected and corrected more easily. In this way, safe data transmissions are possible even with the poorest transmission qualities. This technique opens in a simple way the possibility of reducing the transmission power requirements. This also reduces the energy requirements, thereby providing longer battery life. Another advantage of a two-way, bi-directional digital data transmission lays in the possibility of transmitting test codes in order to filter out external interferences such as, for example, refraction or scatter from the transmission current. In this way, it is possible to reconstruct falsely transmitted data.

The computer device and/or receiving unit of various embodiments of the present invention may be any device known to receive RF transmissions used by those skilled in the art to receive transmissions of data. The computer device and/or receiving unit, by way of example but not limitation, can include a communications device for relaying the transmission, a communications device for re-processing the transmission, a communications device for re-processing the transmission then relaying it to another computer device and/or receiving unit, a computer with wireless capabilities, a PDA with wireless capabilities, a processor, a processor with display capabilities, a desktop computer, laptop computer, tablet computer, smart phone, and combinations of these or like devices known or developed hereafter. Optionally, the computer device and/or receiving unit can further transmit data both to another device and/or back. Further optionally, two different computer devices and/or receiving units can be used, one for receiving transmitted data and another for sending data. For example, with the movement disorder diagnostic system of the present invention, the computer device and/or receiving unit of the present invention can be a wireless router, which establishes a broadband Internet connection and transmits the physiological signal to a remote Internet site for analysis, preferably by the subject's physician. Another example is where the computer device and/or receiving unit is a PDA, computer, tablet or cell phone, which receives the physiological data transmission, optionally re-processes the information, and re-transmits the information via cell towers, land phone lines or cable to a remote site for analysis. Another example is where the computer device and/or receiving unit is a computer or processor, which receives the data transmission and displays the data or records it on some recording medium, which can be displayed or transferred for analysis at a later time.

The digitized kinetic or physiological signal is then transmitted wirelessly to a computer device and/or receiving unit. This computer device and/or receiving unit allows the subject wide movement. Preferably, the computer device and/or receiving unit can pick up and transmit signals from distances of greater than about 5 feet from the subject, more preferably greater than about 10 feet from the subject, even more preferably greater than about 20 feet from the subject, still even more preferably greater than about 50 feet from the subject, still even more preferably greater than about 200 feet from the subject, and most preferably greater than about 500 feet from the subject. The computer device and/or receiving unit is used to re-transmit the signal based in part from the movement or physiological signal from the movement disorder diagnostic device wirelessly or via the internet to another monitor, computer or processor system. This allows the clinician or monitoring service to review the subject's movement or physiological signals and if necessary to make a determination, which could include modifying the patients treatment protocols.

Optionally, the system of the present invention includes some form of instruction, which can be in written form on paper or on a computer monitor, on a video, or communicated via teleconference or videoconference between the subject and clinician, physician or technician. Optionally, a video is used which instructs the subjects to perform a series of tasks during which their kinetic motion and/or EMG and/or other physiological signals related to their motion can be measured. Since the system of the present invention is preferably used in the subject's home, a video giving directions and/or describing various tasks to be performed by the subject is included with the system. The video may be accessed or viewed for example but not by way of limitation through use of video tape, DVD, podcast as part of computer software provided, through the internet, or the like. The directions could include but are not limited to instructions on how to don the device, how to turn the device on, and the like. The description of various tasks could include but is not limited to exercises which are typically used by a technician, clinician or physician to evaluate a subject with a movement disorder including but not limited to hand grasps, finger tapping exercises, other movements and the like. One embodiment of a video includes the technician, clinician or physician looking into the camera, as they would a patient, and instructing them on device setup, instructing the patients through each of the tasks to be performed, providing verbal encouragement via video after a task, and asking subject's to repeat a task if it was not completed. Preferably, these video clips are edited and converted to MPEG or other similar file types either automatically or using editing software. For movement disorders such as Parkinson's disease preferably the technician, clinician or physician instructs the user through multiple tasks as per the UPDRS, tremor rating scale (TRS), or similar scale guidelines including tasks, movements, motions, activities, and the like designed or intended to analyze symptoms of movement disorders including, but not limited to, rest tremor, postural tremor, and action tremor, bradykinesia, rigidity, gait, dyskinesia, and the like. More preferably, if the video is linked to the user interface software, the software will automatically detect if a subject has performed the requested task and provide feedback through the video to either repeat the task or continue to the next task. Still more preferably, once the user has setup the device, it will continually record the subject's movement data (including before and after any directed video tasks), be able to quantify the severity of the subject's symptoms during activities of daily living, and communicate that information with the clinician and subject through interface software, video, or the like.

Also optionally, the subject may not be required or instructed to perform a specific task or group of tasks, but instead measurement and quantification of the subject's symptom(s) and side effect(s) may be carried out while the subject wears the diagnostic device during activities of daily living, or simply on a continuous basis. In other words, the subject may wear the diagnostic device while cleaning his or her home, doing yard work, cooking, exercising, dancing, or other similar activities that are normally performed naturally during the course of one's day. In such embodiments, measurements may be taken periodically according to a predetermined interval or start time, may be initiated by the subject based on the activity he or she is performing or is about to perform, may be initiated by the clinician, physician or technician either in-clinic or remotely, or the system may detect a change in activity or the onset of a symptom or side effect and may automatically perform the measurement, quantification and subsequent steps in order to adjust the therapy device automatically. Where the clinician, physician or technician initiates the process remotely, he or she may interrogate the system via electronic communication means and begin the process.

In many embodiments, an additional step of identifying or determining the activity, movement or motion the subject is performing may need to be determined. As noted, the subject may input a desired activity, movement or motion into the device to begin the assessment and adjustment. In other embodiments, the diagnostic device may detect a change in the subject's activity, movement or motion, or perhaps the onset of a symptom or side effect, and automatically begin the process to adjust the therapy parameters. By way of non-limiting example, if a subject who is wearing the diagnostic device is at work, performing little physical activity, movement or motion, and then leaves work to drive home or exercise at lunch, the system may automatically detect and identify or determine the increased level of activity, movement or motion and also automatically initiate the process of adjusting the therapy parameters to provide better therapy levels allowing the subject to perform the newly altered activity, movement or motion more properly, comfortably and safely. Similarly, if the subject begins to exhibit a change in symptoms or side effects, for example the onset of a tremor, the system may detect and identify or determine the symptom or side effect and automatically initiate the process to adjust or tune the parameters or settings to counteract the symptom or side effect and return the subject to the most normal functionality as possible.

The present invention includes various methods of measuring and scoring the severity of a subject's movement disorder. These methods include a number of steps which may include but are not limited to measuring a subject's external body motion; transmitting wirelessly a signal based in part on the subject's measured external body motion; receiving the wirelessly transmitted signal; downloading data from memory; and scoring the severity of a subject's movement disorder based in part on the wirelessly transmitted or downloaded signal. Optionally, an electromyogram of the subject's muscle activity and/or other physiological signals may be obtained and used in part to score the severity of the subject's movement disorder.

Several preferred embodiments of the present invention include a trained scoring algorithm to determine and provide objective scoring from movement data acquired by the movement disorder diagnostic device. The trained scoring algorithm in part comprises a mathematical model or quantitative representation, used to process kinematic features computed from the movement data and may include some of those steps known to those skilled in the art. In some embodiments of the present invention, the scoring may done on a continuously variable scale of 0-4 with scores substantially similar to or predictive of scores that would be given on the Unified Parkinson's Disease Ratings Scale (UPDRS) by an expert clinician. (“Expert clinician” for the purposes of this application is taken to mean a doctor, nurse, researcher, or other medical or scientific professional trained for and sufficiently experienced in the task of interest, e.g., motor function assessment using the UPDRS, or DBS programming.)

The present invention also preferably includes DBS parameter control methods and tuning algorithms for determining and setting the DBS parameters used to deliver DBS therapy to the subject. In many embodiments, the parameter control methods and algorithms utilize a system of tuning maps, or other parameter display or visualization methods or tools, for DBS programming. Although the term ‘tuning map’ is used throughout this application, it is intended that tuning maps include any such method or tools that may be used for displaying therapy parameters or settings for human review and/or analysis. As noted herein, in the present invention, the tuning maps are preferably only utilized for optional clinician, physician or technician review of the second levels or optimized levels of therapy parameters or settings. The tuning maps utilized by the present invention are a tool used for recording DBS parameters, the subject's response to stimulation at those parameters, and allowing a clinician, physician or technician to optionally or periodically review parameters and settings. Preferably, when utilized, the tuning map is a two-dimensional representation of a three-dimensional graph or display of data.

The subject is preferably first screened and determined to be a viable candidate for DBS therapy, and then has at least one DBS lead surgically implanted into his or her brain. The screening process preferably involves providing the subject with a diagnostic device for monitoring and assessing the subject's movement disorder systems. Generally, a DBS therapy system will include one or more implanted leads with each lead having one or more electrodes. These leads are connected to a pulse generator, which generally is also implanted with the leads. The pulse generator can be implanted in the cranium or preferably in many cases wiring from the leads will be threaded down the subject's neck and the pulse generator will be implanted or embedded in the subject's upper chest or abdomen. The pulse generator will run on a battery, which can in some cases recharged through techniques such as inductive coupling. The DBS therapy system can be adjusted generally through communication between a programming module or unit and the impulse generator. Such a system, as an example, is described in U.S. patent application Ser. No. 12/818,819, filed on Jun. 18, 2010, and Ser. No. 15/210,990, filed on Jul. 15, 2016, which are hereby incorporated by reference. Other systems as known or later developed in the art can also be adjusted with the devices, method and systems of the present invention. Thus, because of the highly invasive nature of therapies such as DBS, requiring surgical implantation of the therapy device, the present invention also optionally provides the screening capabilities to ensure the subject would benefit from such a therapy before undergoing the costly and onerous surgical procedure

The subject may utilize this device at home and during other normal life activities as well, or in a clinical setting, and during the performance of motor and cognitive tests, and while taking his or her prescribed medications for treatment and management of the movement disorder symptoms. The device monitors and records the occurrence of these symptoms and then analyzes the data by means of algorithm(s) designed to account for the multitude of variables including demographic information (age, gender, weight, blood pressure, physical activity, medication use, disease duration, Hoehn & Yahr, marital status/caregiver support, patient expectations, and the like), the type of anticipated DBS therapy (DBS target, unilateral/bilateral implant, constant current versus constant voltage, and the like), non-motor response (UPDRS parts I and II scores, cognition and quality of life assessments, neuropsychology tests, and the like), motor response (UPDRS parts III and IV scores), sensor recordings of symptom occurrence and severity, response to medication, and the like. Generally, subjects who respond favorably to typical medications tend to respond well to DBS therapy. The algorithm analyzes all of the data and makes a determination as to whether the patient is likely to be a good candidate for DBS therapy. The algorithm may optionally employ any one, or a combination of, statistical models currently known to those in the art, including, but not limited to linear and non-linear classification methods such as logistic regression, artificial neural networks, k-means clustering, and the like. The algorithm may output the determination in different ways. In other embodiments, the algorithm may provide a percent likelihood of favorable DBS outcomes for the subject. Still other embodiments may present the multitude of data described above in a chart or graph format in order to allow a clinician, physician or technician to review the data and results and confirm or deny that the subject would benefit from such therapy. By way of non-limiting example, various embodiment may employ a visual display that depicts a pie chart wherein the pie chart is populated showing the measured and quantified occurrences of a symptom(s) such as tremor throughout the day. Any other method known to those skilled in the art for displaying the test results and optionally the input data, or combinations of both, can be envisioned for presenting the data to a clinician, physician or technician for optional review of the screening viability determination. Additionally, the algorithm may provide suggested DBS lead placements in the subject's brain based at least in part on the symptoms and side effects the subject experiences, their severity, and the like. If the subject is determined to be a favorable candidate, the clinician then initiates the process for DBS therapy, which begins with the surgical implantation of at least one DBS lead into the subject's brain. The main purpose of the screening process is to provide a pre-surgical indication of whether the subject would benefit from DBS therapy. This minimizes the likelihood of needless surgery and risk for the subject, as well as time, cost, and resources utilized.

For initial DBS tuning, it may be preferable to perform a monopolar review, which is using a single DBS contact in the monopolar configuration. However, bipolar, or greater, review may be accomplished as well with the present invention. A single DBS lead has several contact points, or electrodes, which can be used to administer the electrical stimulation. Preferably, in many embodiments of the present invention, the DBS leads comprise a plurality of dual-mode contacts which are able to both transmit signals from the lead into the subject's brain, and to detect, acquire or measure signals from the subject's brain (e.g., electrical or electrophysiological signals). A DBS lead may have at least one ground contact, and at least 3 battery contacts for delivering the electrical stimulation, though fewer or more battery contacts may be included. Once the lead, or leads, is implanted into the subject's brain, the subject then undergoes an initial programming session. During the initial programming session, a clinician enters a set of initial test variables (as described above), and administers the electrical impulse to the subject's brain. Typically, the initial test parameters are set and fixed, and then in later iterations the amplitude (voltage or current), as well as other variables are gradually increased or otherwise changed. The results of the impulse may be recorded in a tuning map for optional review indicating the effect which the given set of parameters had on the subject's symptoms, feelings, and the like, and are simultaneously communicated to the tuning algorithm(s). The subject is generally awake for the programming sessions and gives feedback to the clinician regarding any sensations or effects that the subject experiences. The results may include any sort of measured, observed, or calculated response, or combinations thereof including, but not limited to, sensor recordings (for quantifying symptoms as described above), patient responses and perceptions, clinician observations, and clinician scores (e.g., UPDRS, MDS-UPDRS, and the like). In some embodiments, it is possible that the tuning process may be entirely automated such that the tuning map is populated entirely by, and/or the algorithm(s) is supplied with sensor recordings and/or measured and quantified motor symptom data of the subject's response to the DBS therapy and no human observation or calculation is required. Based on the results of the initial test parameters, the tuning map is populated, and the tuning algorithm(s) changes the parameters to more effectively address the subject's symptoms, and the process is repeated. Preferably, in such embodiments, the tuning map is populated simultaneously with supplying the measurements to the algorithm(s), and the tuning map remains a hidden or dormant feature that can optionally be accessed for review of the parameters and settings. Typically, the end result of the tuning process is to optimize the effectiveness of the therapy (i.e., decrease the severity and occurrence of symptoms as much as possible) while minimizing the volume of activated brain tissue, but the particular goals and needs of the subject will dictate exactly what the desired result is for each subject.

Many embodiments of the present invention employ an intelligent system programming capability that greatly decreases the amount of clinician “guess-work” involved in selecting the iterations of DBS parameter values by providing an expert system that efficiently determines appropriate DBS settings. Similar to above, for the first postoperative programming session, the system performs an automated monopolar survey. The subject may wear a motion sensor unit comprising sensors for measuring movement, and performs motor assessments at various DBS settings. Stimulation is incrementally increased from zero at each contact until symptoms stop improving as measured by the motion sensor unit, perceptions, clinician observations or scores, or the like, or until side effects appear as measured by the motion sensor unit, the clinician, or the patient. In many embodiments, adjustments may be based on current rather than voltage since the functional response may be related to the amount of current delivered to a specific target. For constant current IPGs, the current amplitude will be set directly and for constant voltage IPGs, the voltage amplitude may be set based on the required current and impedance measured on the electrode. Preferably, the system is capable of operating in either constant current or constant voltage modes, depending on the clinician's preference and the needs of the particular subject. The monopolar survey helps determine the functional anatomy around the DBS lead site and narrows the search space for determining an optimal set of programming parameters. A therapeutic window will be defined as the region in which a patient exhibits optimal symptomatic benefits without side effects. This therapeutic window will be valuable at the initial postoperative programming session as well as all future adjustment sessions for determining the current amplitude when side effects begin to occur on each contact. This therapeutic window is then used to define a side effect region. The system includes internal electric field modeling to determine how this side effect region can be avoided, possibly by shaping the electric field with a bi- or tripolar configuration or altering the pulse width. Bipolar or tripolar configuration refers to the simultaneous delivery of electrical impulses from two or three, respectively, contacts on the DBS lead to shape the electrical field delivered to the subject's brain. For monopolar stimulation, current falls off proportionally to the distance from the negative electrode contact. For bipolar stimulation, current decreases proportional to the square of the distance to the negative contact, but increases by the square of the distance between the negative and positive contacts. Efficient stimulation algorithms are used to find a set of parameters that optimize for efficacy while minimizing side effects and battery usage. The patient and/or clinician will be able to give higher weight to a given item, parameter, or symptom (e.g., tremor severity) that may be most important to him or her. Many clinicians are ignorant of the battery voltage of the IPG battery and therefore unaware that a slight increase of the stimulation amplitude above the battery voltage will activate voltage doubler or tripler circuits in the IPG, significantly increasing battery drain and shortening battery life by half. The algorithms will automatically avoid increasing voltage above the battery voltage unless necessary for finding a therapeutic window. After the automated monopolar survey is completed and a patient-specific functional map is developed during the initial postoperative programming visit, subsequent programming adjustments will be much simpler and faster.

Basing an optional and one of the preferable algorithm(s) on functional, rather than on structural, anatomy has several advantages. First, most DBS programmers are not imaging experts and may not have the wherewithal to correctly interpret complex anatomies. More importantly, the therapeutic mechanisms of DBS are largely unknown. The optimal stimulation location differs across patients and is based on functional rather than structural anatomy. Therefore, the system will be individualized to each subject's response and be far superior to recently developed DBS programming aids, which rely on anatomical assumptions, imaging, and statistical modeling to estimate the electric field at various anatomical targets.

The intelligent system programming capability takes the results of the initial test parameters and automatically populates the tuning map while simultaneously providing these results to an algorithm(s). The system's tuning algorithm(s) analyze these results and provide the next iteration of DBS parameters. These provided parameters or settings may be reviewed and possibly edited by the clinician, physician or technician, or may be automatically entered into the subject's therapy device thus programming the therapy device to operate according to those settings. Effectively, the system provides optimized DBS parameters or settings which either are automatically implemented, or may act as a guide for the clinician in setting the IPG for the next iteration of testing (particularly for the initial programming session). This reduces the clinician's need to perform the analysis and determine which parameters to change and how much to change them. The clinician may have the option to edit or elect any one or combination of the system's suggested parameters for the next iteration. This intelligent programming system may be performed in-clinic during a traditional programming appointment, whereby the system provides the suggested DBS parameters or settings to the clinician and the clinician can review and edit the parameters or settings through the software which in turn adjusts the settings and parameters on the IPG. Alternatively, the intelligent programming may take place remotely whereby the system automatically and intelligently provides optimized parameters or settings and programs them into the subject's therapy device, or communicates any data and suggested DBS parameter settings to the clinician, physician or technician who is located some distance away while the subject remains at home, and the clinician then reviews and elects to approve or edit the suggested settings, which are sent to the IPG to update the parameters of the DBS administered to the subject, or still further the clinician may instruct the diagnostic device to perform another iteration of testing, rather than editing the suggested parameters or settings herself. Where the clinician, physician or technician does perform the option or periodic review, the data (e.g., parameters and settings, test results, and the like) may be displayed for his or her review in a tuning map as described. Because the clinician is not required to be physically present at the time of programming, the system instead may rely on system and user reports, which are sent to the clinician. These reports may be made by the system sending reports to the clinician, video and/or audio conferences between the subject and the clinician, the subject keeping a medication diary to report medication schedules and symptom occurrence and severity, transmitted tuning maps, and the like.

Some embodiments of the present invention provide the clinician, physician or technician the ability to manually make the determination as to what therapy parameters to use with the subject's therapy device based in part on the tuning map or data corresponding to the subject's measured and quantified motor symptoms or based on other data measured by the movement disorder diagnostic device from the subject. Again, this is solely an optional review designed for periodic analysis of the algorithm(s)′ function and to ensure that the subject's needs are being met adequately and safely. The present invention further optionally allows the clinician, physician or technician the ability to review recommended second level therapy parameters before or after those therapy parameters or settings are entered into the therapy device and to change those recommended settings.

The present invention includes intelligent remote programming methods and algorithms utilizing a database, which may be cloud-based in some embodiments, allowing for remote DBS adjustments being possible without the subject even leaving his or her home, and in some intelligent embodiments without clinician involvement. The system may provide automated, objective scoring and tuning algorithms to take the programming expertise out of the hands of a clinician and perform it remotely, and enable high-quality programming to all DBS recipients, regardless of proximity to or availability of expert programmers. Ideally, the subject would not have to travel to the clinic or facility for programming unless a problem was detected requiring personal medical care. Such embodiments necessarily include the integrated system programming capability whereby the software directly communicates with the hardware to set the DBS parameters according to the values entered into the software in order to enable the periodic or optional review by the clinician, physician or technician. For such optional or periodic review, rather than in-clinic programming sessions, the software would communicate with the DBS hardware, which is located remotely, implanted into the subject. In these embodiments, the implanted device may perform an intelligent system analysis, creating a suggested set of DBS parameters for the particular subject, and then securely communicate all the data and suggested parameters to a centralized or cloud-based database, which analyzes all the information and then sends programming commands to the subject's IPG to change the DBS settings. This database and intelligent remote system allows for continued or repeated DBS tuning without requiring the patient to travel to a clinician, and without requiring a clinician to spend the time analyzing subject data. The benefits of such a system go beyond the convenience of minimizing travel time and access to clinicians and include the ability to deliver such reports at virtually any time (the subject and clinician are not tied to a particular appointment time and window, and the clinician can review any data at any time if desired), continuous and repeated monitoring of the subject's and system's statuses, and delayed monitoring whereby results of changed parameters can be monitored later, which is particularly useful for symptoms that may not react to changes rapidly (i.e., during the normal clinical appointment time period).

Further, the system preferably includes the capability to perform hardware diagnostic tests remotely of any and all of the individual units or modules used with the present invention, including the subject's therapy device (e.g., implanted pulse generator), movement disorder diagnostic device, programmer unit, and the like. The hardware in such embodiments is able to monitor and/or periodically interrogate the system to detect changes in system conditions such as battery status, electrode impedance, and the like. The system then sends the results of these diagnostic tests back to the clinician who can monitor them to determine if a problem arises requiring the subject to return to the clinic for adjustments, repairs, or other such purposes. Further still, such embodiments using remote programming and control may include medication delivery systems as well. Such delivery systems include a drug reservoir for holding and storing medication, and infusion pump for delivering said medication from the reservoir to the subject. Such embodiments may determine based on recorded signals that the subject's symptoms are particularly severe or occurring more frequently.

Some embodiments may optionally include a closed-loop or semi-closed loop drug titration system. In such embodiments, when the subject's prescribed medication is initially taken or delivered, the system then monitors the subject's symptoms. The system continues to monitor the symptoms until and after it detects that the subject's symptoms increase, maintain, or only very slightly decrease in severity and rate of occurrence. In a semi-closed loop system, a report, warning, alert, or some other signal would then be sent to the subject or to the subject's clinician. In such case, the subject could take more medication, or the clinician could send a command for an integrated drug delivery pump to administer another dose. In a close-loop system, upon detection of the above indicators, the system would automatically administer an additional dose of medication through an integrated medication delivery pump. In either case, the system is capable of substantially continuous symptom monitoring to determine when the subject is experiencing an increase in symptom activity, ineffective medication delivery, or a wearing off of medication in order to administer additional medication to control the subject's symptoms. Further, such embodiments must also be able to monitor and detect the occurrence of side effects arising from the medication, and to stop administering medication when such side effects begin to manifest.

It will be further noted that use of the device and method of the present invention in combination with treatment directed at stopping or slowing the progression or onset of a movement disorder is intended to optionally include the use of the movement disorder monitoring device with a broad scope of pharmaceutical agents and/or other treatments directed at stopping or slowing the progression or onset of a movement disorder. Neuroprotective drugs provide one specific example of a compound that can be used to stop or slow the progression or onset of a movement disorder. Briefly stated, neuroprotective drugs include a broad set of compounds that serve to eliminate or reduce neuronal death in the central and/or peripheral nervous systems, hence eliminating certain movement disorder symptoms that can follow neuronal death and stopping progression or onset of a movement disorder disease. By way of specific example, in the case of PD certain drugs have been and are being examined and may be found to be effective at eliminating or reducing death of a subject's dopamine producing neurons, and the efficacy of such drugs over extended periods of time could be objectively monitored using the device and method of the present invention as a means to collect and review movement disorder symptom data over extended periods of time. By way of example, neuroprotective drugs that have been and are being examined for their potential in stopping or slowing the progression of movement disorders such as PD include drugs such as selegiline, riluzole and lazabemide. It is to be understood that the scope of the present invention is intended to cover the use, with the device and as part of the method of the present invention, of these drugs as well as other neuroprotective drugs that may yet be discovered or are currently under investigation.

Various embodiments of the present invention further include steps of mapping the subject's brain activity and corresponding said brain activity to movement the subject is performing or has performed. Such movement may be voluntary or involuntary, and/or normal or symptomatic. In order to understand the brain mapping process, it is important to understand the basic structures of the nervous system and how they interact. The neuron doctrine provides a descriptive and informative set of information regarding neurons, the nerve cells tasked with transmitting nerve impulses within the nervous system. The neuron doctrine states that the brain is made up of individual cells (neurons) that are very different from other cells and tissues in the body, partially in that neurons comprise specialized features such as dendrites, a cell body, and an axon, structures not contained in any other type of cell in the body. Like most other cells, neurons contain a nucleus, and the neuronal nucleus must have access to nutrition for the cell to remain alive; if one were to split or divide a neuron, only the portion with the nucleus will survive. Neurons can vary greatly in size, shape and structure as determined by their locations or particularized and specialized function or role within the body. Nerve fibers are the result of cell processes and the outgrowths of nerve cells whereby several axons are bound together to form one nerve fibril, or neurofilament. Several nerve fibrils then form one large nerve fiber. Myelin, an electrical insulator, forms around selected axons to insulate and protect the axon. Adjacent or connected neurons are connected to each other, not by continuity of cytoplasm or physical contact between the cells, but rather by a contact point across which the cells communicate by transmission of chemical or electrical signals via structures called synapses. The axon, the transmission end of a neuron, is capable of transmitting such signals as well as receiving them, but the system exhibits a preferred direction of communication from cell to cell—typically transmission from axon of one neuron and receipt of the signal by the dendrite of the next neuron—and this is called the Law of Dynamic Polarization. Once two neurons make contact, a unity of transmission exists such that contact can be either excitatory or inhibitory, but will always be the same type of transmission and will never switch, and each nerve terminal releases a single type of neurotransmitter. Thus, when the signal is transmitted via the synapse between two neurons, it either excites the neuron into cueing an action, or inhibits the neuron and prevents or stops ongoing action—such as movement of a muscle. These communications between neurons not only control the body's movement and functions, but also form thoughts, feelings, and the like. A very specific set of neurons, staring in the subject's brain, must fire in a specific order to achieve the end result, for example performance of a particular movement. Like all other tissues in the body are composed of individual cells, these neurons are the individual cells that make up the various portions of the nervous system including the brain and spinal cord. Various parts of the brain (hemispheres, lobes, cortexes, etc.) and the spinal cord are employed for many different functions of the body. With particular regard to movement, the brain comprises a motor cortex which is involved in virtually all movement. The primary motor cortex, or M1, is one of the principal brain areas involved in motor function. M1 is located in the frontal lobe of the brain, along a bump called the precentral gyms. The role of the primary motor cortex is to generate neural impulses that control the execution of movement. Signals from M1 cross the body's midline to activate skeletal muscles on the opposite side of the body, meaning that the left hemisphere of the brain controls the right side of the body, and the right hemisphere controls the left side of the body. Every part of the body is represented in the primary motor cortex, and these representations are arranged somatotopically—the foot is next to the leg which is next to the trunk which is next to the arm and the hand. The amount of brain matter devoted to any particular body part represents the amount of control that the primary motor cortex has over that body part. For example, a lot of cortical space is required to control the complex movements of the hand and fingers, and these body parts have larger representations in M1 than the trunk or legs, whose muscle patterns are relatively simple. This disproportionate map of the body in the motor cortex is called the motor homunculus. Other regions of the cortex involved in motor function are called the secondary motor cortices. These regions include the posterior parietal cortex, the premotor cortex, and the supplementary motor area (SMA). The posterior parietal cortex is involved in transforming visual information into motor commands. For example, the posterior parietal cortex would be involved in determining how to steer the arm to a glass of water based on where the glass is located in space. The posterior parietal areas send this information on to the premotor cortex and the supplementary motor area. The premotor cortex lies just in front of (anterior to) the primary motor cortex. It is involved in the sensory guidance of movement, and controls the more proximal muscles and trunk muscles of the body. In our example, the premotor cortex would help to orient the body before reaching for the glass of water. The supplementary motor area lies above, or medial to, the premotor area, also in front of the primary motor cortex. It is involved in the planning of complex movements and in coordinating two-handed movements. The supplementary motor area and the premotor regions both send information to the primary motor cortex as well as to brainstem motor regions. Neurons in M1, SMA and premotor cortex give rise to the fibers of the corticospinal tract. The corticospinal tract is the only direct pathway from the cortex to the spine and is composed of over a million fibers. These fibers descend through the brainstem where the majority of them cross over to the opposite side of the body. After crossing, the fibers continue to descend through the spine, terminating at the appropriate spinal levels. The corticospinal tract is the main pathway for control of voluntary movement in humans. There are other motor pathways which originate from subcortical groups of motor neurons (nuclei). These pathways control posture and balance, coarse movements of the proximal muscles, and coordinate head, neck and eye movements in response to visual targets. Subcortical pathways can modify voluntary movement through interneuronal circuits in the spine and through projections to cortical motor regions. The spinal cord is comprised of both white and gray matter. The white matter consists of nerve fibers traveling through the spine. It is white because the nerve fibers are insulated with myelin for faster conduction of signals. Like many other large fiber bundles, the corticospinal tract courses through the lateral white matter of the spine. The inside of the spinal cord contains gray matter, composed of the cell bodies of cells including motor neurons and interneurons. In a cross-section of the spinal cord, the shape of the gray matter resembles a butterfly. Fibers in the corticospinal tract synapse onto motor neurons and interneurons in the ventral horn of the spine. Fibers coming from hand regions in the cortex end on motor neurons higher up in the spine (in the cervical levels) than fibers from the leg regions which terminate in the lumbar levels. The lower levels of the spine therefore have much less white matter than the higher levels. Within the ventral horn, motor neurons projecting to distal muscles are located more laterally than neurons controlling the proximal muscles. Neurons projecting to the trunk muscles are located the most medially. Furthermore, neurons of extensors (muscles that increase the joint angle such as the triceps muscle) are found near the edge of the gray matter, but the flexors (muscles which decrease the joint angle such as the biceps muscle) are more interior. It is important to note that a single motor neuron in the spine can receive thousands of inputs from the cortical motor regions, the subcortical motor regions and also through interneurons in the spine. These interneurons receive input from the same regions, and allow complex circuits to develop. Signals generated in the primary motor cortex travel down the corticospinal tract through the spinal white matter to synapse on interneurons and motor neurons in the spinal cords ventral horn. Ventral horn neurons in turn send their axons out through the ventral roots to innervate individual muscle fibers. In this example, a signal from M1 travels through the corticospinal tract and exits the spine around the sixth cervical level. A peripheral motor neuron relays the signal out to the arm to activate a group of myofibrils in the bicep, causing that muscle to contract. Collectively, the ventral horn motor neuron, its axon, and the myofibrils that it innervates are called a single motor unit. Each motor neuron in the spine is part of a functional unit called the motor unit. The motor unit is composed of the motor neuron, its axon and the muscle fibers it innervates. Smaller motor neurons typically innervate smaller muscle fibers. Motor neurons can innervate any number of muscle fibers, but each fiber is only innervated by one motor neuron. When the motor neuron fires, all of its innervated muscle fibers contract. The size of the motor units and the number of fibers that are innervated contribute to the force of the muscle contraction. There are two types of motor neurons in the spine, alpha and gamma motor neurons. The alpha motor neurons innervate muscle fibers that contribute to force production. The gamma motor neurons innervate fibers within the muscle spindle. The muscle spindle is a structure inside the muscle that measures the length, or stretch, of the muscle. The golgi tendon organ is also a stretch receptor, but it is located in the tendons that connect the muscle to the skeleton. It provides information to the motor centers about the force of the muscle contraction. Information from muscle spindles, golgi tendon organs and other sensory organs are directed to the cerebellum. The cerebellum is a small grooved structure located in the back of the brain beneath the occipital lobe. This motor region is specifically involved when learning a new sport or dance step or instrument. The cerebellum is involved in the timing and coordination of motor programs. The actual motor programs are generated in the basal ganglia. The basal ganglia are several subcortical regions that are involved in organizing motor programs for complex movements. Damage to these regions result in spontaneous, inappropriate movements. The basal ganglia send output to other subcortical brain regions and the cortex.

Understanding such structure and function of neurons facilitates brain mapping by the knowledge of what is happening within the brain and nervous system as it appears through, for example, various neuroimaging techniques during the mapping process. Within the scope of the present invention, particularly focusing on movement disorder therapy, monitoring the brain as a subject performs particular movements or experiences particular symptoms can help provide a framework and guide to the functions of the subject's brain as they correlate to such movements or symptoms. Electrochemical responses may be observed and recorded to indicate neuronal activity. As neurons are activated, they exhibit a hemodynamic response in the form of rapid delivery of blood to active neuronal tissues and the neuron transmits a Blood Oxygenation Level Dependent (BOLD) signal that corresponds to the concentration of deoxyhemoglobin within the neuron. Thus, as numerous neurons in a particular area of the brain are activated, they BOLD signal increases for that area based on the increased blood flow to each of the active neurons there. Imaging modalities may then be used to detect this increased blood flow, for example fMRI, and can then provide an image of the areas of the subject's brain that are activated in response to a particular stimuli or variable. Another option is to observe and record electrical responses to neuronal activity. Evoked electrical potentials, generated from the occurrence of a specific event or application of a catalyst, can be measured using sensors such as electrodes (implanted or non-invasive, surface electrodes), such as with use of an electroencephalogram (EEG). Multiple types of evoked potentials are known and characterized based on the response-eliciting event, be it sensory (somatosensory), cognitive or motor in origin. Positive and negative voltages may be recorded by the electrodes, typically in the range of 10μ to 100 μvolts, and voltage swings are separated into positive and negative values, as well as by time in which they were recorded after the event or stimulus has occurred. The voltages may alternatively be labeled in order and characterized as positive or negative. The voltages are representative of the output or activity of the neurons in response to the stimulus or event (e.g., particular movement). Still another alternative may be to map magnetic fields produced from electrical currents that naturally occur in the subject's brain in order to resolve time courses of brain activity. Magnetoencephalography, but measuring these magnetic fields, is capable of resolving events in 10 milliseconds or less, and is particularly suited for brain activity mapping in the primary auditory, somatosensory and motor cortexes and areas. Each of these, and other imaging modalities can be used alone, or in combination with each other, in order to identify portions or structures of the subject's brain that are activated or deactivated by a particular event or occurrence, such as a particular movement or movement disorder symptom.

It is important to note that within the scope of the present invention, brain mapping is a separate and wholly different concept than that of the tuning maps described herein. Tuning maps are visual representations of objective scores indicating the severity of a subject's movement disorder symptoms as a result of numerous variables including therapy stimulation parameters. Brain mapping, and resultant brain activity maps, are maps of measured or detected electrical activity of the brain indicating the location and strength or intensity of such activity of electrical activity of the brain. The tuning maps are a graphical representation of calculated symptom severity scores as calculated by one or more algorithms as a result of measured signals from sensors whereas the brain maps or brain activity maps are representations of measured electrical activity of the brain directly obtained (possibly with some pre-processing and processing) directly from sensors.

Brain mapping, for the purposes of the present invention, can be performed by various imaging methods, each with varying degrees of invasiveness. Surface imaging techniques, such as EEG signals acquired via surface sensors or electrodes, is a common method of detecting and mapping a subject's brain activity. The surface-acquired EEG signals may be localized to a general or more particular region of the brain from which the electrical activity is sensed and acquired, and thus a map may be generated providing an indication of the regions of the brain that are excited and utilized at various points in time. Another method, more invasive than surface EEG or other such surface imaging techniques, is electrocorticogram (ECoG) signal acquisition. Like EEG, ECoG also can be used to acquire brain activity signals, but ECog involves the implantation of sensors or electrodes directly onto the surface of the subject's brain. Because of the implanted nature of the electrodes used for obtaining EEG signals, ECoG is sometimes referred to as intracranial electroencephalography (iEEG); it acquires essentially the same signals as traditional, surface EEG but with implanted brain surface electrodes as opposed to external surface electrodes. Given the direct placement of ECoG sensors on the surface of the subject's brain, ECoG may be more accurate and precise in the measurement of brain activity that traditional EEG due to the additional layers of tissue (cranium, blood, skin) that separate the subject's brain from traditional EEG sensors, where ECoG can measure directly from the surface of the brain. Either type of imagine may be used in order to obtain brain activity data, however, and both are commonly accepted methods of acquiring such information. Accordingly, brain mapping for the purposes of the present invention may be performed using either of these, or other such imaging techniques known to acquire and monitor brain activity from the external surface or internally from the surface of the subject's brain. While brain activity can be monitored and mapped by external surface electrodes or internal, brain surface electrodes, the resolution of such images is generally low, and is certainly not sufficient to get a clear picture of what specific neurons or bundles of neurons are operating to create a certain motion or cause a particular symptomatic movement. Low resolution brain mapping is useful for general brain-region excitation identification, but much higher resolution is required to truly perform a safe, accurate and efficacious programming or tuning function for therapy devices. Therefore, the present invention preferably employs implanted sensors located within the subject's brain to detect and acquire signals relating to activation of the neurons within the subject's brain. Electrocorticography systems utilize implanted sensors on the surface of the subject's brain, and have been used to sense brain activity in order to map such activity. Electrocorticography systems, however, acquire signals from a vast amount of neurons at once, on the order of hundreds of thousands of neurons, and thus the resolution is still too low for discernment of targeted brain regions, structures, and neurons that are attributable to a given even, such as a particular movement. Embodiments of the present invention that employ brain-mapping to facilitate the therapy delivery process preferably obtains higher resolution brain mapping images, correlated to smaller bundles of neurons or even individual neurons that correlate to movements or symptoms of interest. In order to obtain such high resolution brain mapping images, such embodiments of the present invention may utilize either separate electrodes, sensors or leads for acquiring brain activity data, or dual-mode stimulation/sensing leads implanted into the subject's brain. More specifically, the DBS electrodes each preferably comprise multiple contacts disposed along the length of each DBS lead where each contact is not only capable of providing an electrical current stimulation to the subject's brain, but is also capable of sensing or acquiring signals from the subject's brain. Such acquired signals are preferably representative of brain activity in the immediate vicinity of the contact's location within the subject's brain such as local field potentials or single-/multi-units responses. Thus, the present invention utilizes dual-mode DBS leads which comprise multiple contacts each capable of both sensing signals and providing electrical stimulation.

Thus, the systems and methods of the present invention may include the step of mapping the subject's brain activity, utilizing the above disclosed methods or those known in the art, in order to determine the areas and regions, potentially down to the level if bundles of neurons or individual neurons, that are employed to elicit a certain action or event from the subject, such as movement. Initial, or any subsequent, brain mapping performance may be performed in a guided session whereby the subject is instructed to perform a specific movement or task while the dual-mode DBS leads acquire signals from the brain to determine what part(s) of the brain caused that motion or activity to occur. Although directed and instructed movements may be used, preferably the subject's movement is simultaneously measured by external sensors such as described herein. In this manner, an initial programming session may eliminate the variables of discretionary or complex movements, and allow the system to measure external body motion with the sensors while simultaneously acquiring brain activity data to correlate the brain activity with the known, instructed motion as well as with the measured external body motion obtained from the external sensors. In this manner, a baseline can be formed by which the system identifies and recognizes particular motions of the subject's body and measures them using the external sensors, and then “knows” based on the correlation with the brain activity map what motion(s) or movement(s) are being performed and which areas of the brain are used to cause them.

The use of known, directed or instructed tasks is not, however, necessary, given that the external body motion sensors alone may be sufficient to identify particular movements or motions and to create the correlation with brain activity. As the system is used over a period of time, particularly in real-life settings of the subject performing activities of daily living with the external sensors measuring the subject's external body motion and the implanted dual-mode DBS leads measuring brain activity, the system can preferably learn from such continuous use and associate patterns of brain activity with specific external boy movements. Preferably the system comprises nonvolatile internal memory that can be used to store data related to the external body motion as well as the brain mapping data, creating a database comprising the data with marking information (e.g. time data) so that the data can be cross-referenced to see the correlation. Preferably the correlation is also calculated, using a movement and brain activity algorithm in real-time and stored along with the data related to external body motion and bran map activity. Iterative movements and measurements can thus be correlated and later accessed and/or compared to determine what movement the subject was performing or what areas of the subject's brain were responsible for movement. As the system monitors more and more complex movements over time, a large database may be compiled that effectively links individual components of a subject's movement (e.g., movement of the index finger vs. movement of the whole arm) to the portions of the brain that are responsible for causing such components of movement. By extension, the external body motion sensors will not only measure calculated, normal and deliberate movement, but will also capture involuntary and instinctive movement, as well as symptomatic movement. Repeated iterations of the measurements of both body movement and corresponding brain activity will help the system resolve a distinct and accurate map of the particular portions, structures, or even neurons of the subject's brain that cause each to type of movement to occur, including symptoms.

This learning process can greatly be facilitated by an initial training protocol such as described above wherein the system is used to measure both external body movement and corresponding brain activity under known conditions while the subject performs directed, instructed movements, tasks or activities. The baseline created from the training data set creates an initial data set where the particular movement is known and can be immediately correlated and identified as being caused by the measured and observed brain activity measuring during performance of the protocol. The movement and brain activity correlation algorithm(s) (correlation algorithms) are thus preferably machine learning algorithms designed to see the relationship between two very large bodies of data. One example of types of correlation algorithm that can be used is a supervised algorithm whereby the algorithm comprises a target or outcome variable (or dependent variable) (such as a correlation index or qualified definition that certain movements are caused by certain portions, structures or neurons of the brain) which is to be predicted from a given set of predictors (the measured external body movement data and brain activity/brain map data). Using these set of variables, the algorithm generates a function that maps inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data as indicated by accurate identification of the brain portions that cause the movement. Examples of supervised learning algorithms include regression algorithms, decision tree algorithms, random forest algorithms, k-nearest neighbor (KNN) algorithms, logistic regression algorithms, and the like. Another example would be unsupervised algorithms in which target or outcome variable to predict or estimate is required. Unsupervised algorithms are best used for clustering populations in different groups, such as segmenting types of movement into identifiable groups with identifiable characteristics (e.g., arm movements, left hand movements, etc.). Examples of unsupervised learning algorithms include the Apriori algorithm, K-means clustering algorithms, and the like. Still another example of types of algorithms that may be used as correlation algorithms within the scope of the present invention includes reinforcement learning algorithms with which the machine is trained to make specific decisions whereby the machine is exposed to an environment where it trains itself continually using trial and error (such as by comparing calculated correlations with known correlations obtained during an initial training session). The system learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Examples of reinforcement learning algorithms include the Markov Decision Process. This, and other algorithms known to those skilled in the art, can be used alone or in combination to help identify from the various sets of measured data what portions of the brain cause certain movements to occur—either normal or symptomatic.

As the system is used continually over a period of time, the amount of measured data and the correlations between the myriad measured data preferably create a vast and robust trained algorithm tied to the database that is able to identify in real-time a movement or motion from measured brain activity in a particular pattern, or to identify the areas of the brain that caused a particular detected or measured movement to occur. The database may be local within the one or more components of the device, or may be a remote or cloud-based database. Preferably, the system and algorithm(s) are also able to discern between normal and symptomatic movement, thus identifying symptomatic movement as the portions of the brain that might be contributing to the occurrence of symptoms or the over-arching disease or disorder. Ultimately, in some embodiments, after sufficient training of the algorithm has occurred, the external movement sensors may be able to be removed from operation of the system. In such embodiments, the system would be able to identify the occurrence of a particular movement or motion—most notably symptomatic movement or motion—based solely on a particular pattern of brain activity.

The brain mapped data may then be used in the therapy tuning processes described herein. Again, the dual-mode DBS leads, each preferably with a plurality of contacts, are used not only to sense brain activity (which is then correlated with external body movement sensor data and our known inputs related to prescribed movement), but to provide electrical stimulation to the subject's brain. The exact form of the electrical stimulation provided may be influenced by measured movement from external body motion sensors as well as the measured or detected brain activity from the dual-mode DBS leads. Based on either measured external body movement and/or sensed brain activity the system preferably outputs a desired set of therapy parameters or settings tailored to address the particular state of the subject. In the present embodiments utilizing brain activity mapped data, the process is essentially similar to that described throughout regarding measured movement data, tuning maps, and the like, but with either brain-mapped data instead of the externally-sensed movement data and/or tuning maps, or in conjunction with such measured movement data. The subject's movement and/or brain activity are measured and symptoms severity is scored based on the measured symptomatic movement, or identified symptomatic movement in the brain activity data. Again, the system is able to identify such symptomatic movement after correlating movement data and/or other physiological sensor data with sensed brain activity for the particular patient. Based on the symptom severity, the system may provide an output comprising a new or second level of therapy parameters or setting that may be applied to the subject's DBS device in order to provide such therapy to the subject. Embodiments utilizing mapped brain activity data may be able to provide more accurate and finely-tuned therapy stimulation to the subject. This increased accuracy and precision is due to the high-resolution brain activity mapping that causes the present invention to exhibit a more educated and precise identification of the parts of the subject's brain which are related to the symptomatic movement. Thus, utilizing mapped brain activity data, not only can the attributes of the stimulation (e.g., amplitude, frequency, etc.) be adjusted to address the target area of the brain, but the stimulation field can be shaped more precisely and accurately in order to target that portion of the brain and minimize the stimulation's effects on other areas of the brain, and thus also minimize occurrence of side effects. Thus, brain activity mapping may be used to augment and enhance the external movement measurement embodiments described herein, either open loop with clinician input, semi-closed loop, or closed loop.

Additionally, it is envisioned that, after time using the system and building a strong and robust database of mapped brain activity correlated to measured and/or known movement data, other embodiments may not require or utilize external body movement sensors, and may rely solely on the dual-mode DBS leads. In such embodiments, the system senses and maps brain activity, and when such brain activity indicates that symptomatic movement is occurring, decides whether to adjust the therapy parameters in order to address that symptomatic movement. As with the other embodiments, the suggested therapy parameters may be output for clinician review, or may be automatically implemented and programmed into the subject's DBS device. Such embodiments allow for real-time, finely-tuned and specifically targeted DBS therapy to be delivered as a substantially immediate response to the detected occurrence of the symptomatic movement at the origination point in the subject's brain.

The above systems, devices, and methods are further contemplated for use in treating various mental health disorders, particularly major depression, bipolar disorder, and obsessive compulsive disorder. In particular treatment of mental health disorders would benefit from the patient screening system and method for determining if the patient would benefit from DBS therapy in dealing with his or her disorder, as well as the integrated and intelligent programming systems and methods for both in-clinic and remote programming of the DBS device once implanted.

Now referring to the drawings and figures, FIG. 1 illustrates the therapeutic device programming (or “tuning,” or “parameter settings adjustment”) process with one embodiment of the invention. A subject 1 has a therapy device (not shown), which in the illustrated case is a therapy device for the treatment of a movement disorder, such as an implanted DBS device. Subject 1 wears a movement disorder diagnostic device comprising a sensor unit 2 and a command module 3. The sensor unit 2 comprises at least one sensor(s), preferably a physiological or movement sensor(s), such as accelerometers and/or gyroscopes (both not shown), or other similar sensors, as well as a transmission system (not shown). In one preferred embodiment, the sensor unit 2 comprises three orthogonal accelerometers and three orthogonal gyroscopes, or more preferably at least one 3-axis accelerometer and at least one 3-axis gyroscope. Preferably, where the at least one sensor is an accelerometer and/or a gyroscope, these are micro-electrical-mechanical (MEMS) accelerometers or gyroscopes. In a preferred embodiment, a single chip containing both a 3-axis accelerometer and a 3-axis gyroscope is used, rather than using separate sensors. An example of such a combined sensor chip is the Invensense MPU-6000. The transmission system may be wired or wireless, and may communicate via any medium and any transmission protocol known to those skilled in the art. In the illustrated embodiment, the sensor unit 2 communicates sensor readings to a command module 3 over a small flexible transmission cable 4, though this transmission could also be conducted wirelessly. In the more preferred embodiment where both the sensor unit 2 and command module 3 are combined in a single housing constituting the entire movement disorder diagnostic device, the two modules may be integrated into the same electronics thus eliminating the need for wired or wireless communication between separate modules. In the illustrated embodiment, the sensor unit 2 is worn on the middle phalange of the middle finger and the command module 3 is worn on the wrist using a wristband, though the placement of the sensor unit 2 and command module 3 may vary depending upon the symptoms of the movement disorder. Alternate placements could include other fingers, the ankle, foot, shoulder, or elsewhere on the trunk of the body or on any part of any extremity. While the illustrated embodiment shows the sensor unit 2 and the command module 3 as having separate enclosures, permitting for a lighter-weight sensor unit 2 that is easily worn on the finger, in alternate embodiments the sensor unit 2 and command module 3 may be integrated into a single enclosure. In such embodiments where the sensor unit and command module are combined into a single enclosure forming the movement disorder diagnostic device, all components of each unit are enclosed or attached to the single enclosure, including, but not limited to the units and modules themselves, any power supply and communication electronics required for operation.

The command module 3 may provide numerous functions including, but not limited to supplying power to the sensor unit 2, storing data in memory, transmitting data. Preferably, it is controlled by firmware in processor, for example an Analog Devices ADuC7020 processor. The data acquisition (DAQ) section samples finger sensor unit data at 128 Hz for each of the six channels. Optional onboard memory preferably provides at least 12 hours of data storage. Some embodiments do not contain internal storage, but rather transmit the data substantially in real-time to a receiver unit 5, a centralized database (not shown) or to a cloud-based database (not shown). Still other embodiments utilize onboard, temporary data storage as well as substantially real-time data transmission to a receiver unit 5, centralized database (not shown) or a cloud-based database (not shown). A lithium-based battery provides at least 12 hours of continuous use and is rechargeable by a computer through a LEMO, or similar connector, to USB connector cable. The command module 3 also integrates a membrane switch label (not shown) with LED indicators for power and charging (not shown). Three membrane switches inside the label (not shown) provide on/off control and two subject diary inputs. The command module 3 may perform rudimentary signal processing, such as filtering and analog-to-digital conversion, on the movement signals received from the sensor unit 2 before transmitting the movement signals to a receiver unit 5. The receiver unit 5 may be of any type known to those skilled in the art, and useful for receiving data from the sensor unit and making it available to a clinician, physician or technician on computer device 6. The computer device 6 will be referred to as a tablet (or tablet computer), but it is meant to be understood that it may be any such similar device, including, but not limited to desktop computers, laptop computers, tablet computers, personal digital assistants (PDAs), “smart” cellular telephones, or the like. This transmission may be wired, but is preferably wireless, advantageously providing the subject the greater comfort and convenience of being untethered as well as endowing the system with enhanced safety and portability. The wireless link frees subject motion, which allows unimpeded and accurate assessment of subject symptoms. In an operating room, a small untethered system has the added benefits of reducing further subject discomfort and not impeding clinical traffic. A wireless system, which is not directly connected to any source of AC power, has the added benefit of reducing or eliminating risk of electrical shock. Preferably, the wireless transmission is robust and operates in a frequency band designated for hospital or clinical-setting use. Preferably, the wireless transmission radio is a Bluetooth radio operating in the 2.4 GHz band. More preferably, radio transmission occurs over the Wireless Medical Telemetry Service (WMTS), dedicated by the FCC to wireless medical equipment used in hospitals, which comprises the frequencies 608 to 614 MHz, 1395 to 1400 MHz and 1429 to 1432 MHz. Preferably, radio communication is accomplished using a mix of traditional heterodyning techniques along with newer software radio techniques. For example, receiver structure consists of a band select function of either 608-614 MHz or 1395-1432 MHz, followed by a heterodyning operation. The lower frequency band undergoes one frequency translation while the upper undergoes two frequency translations. For the low band (608-614 MHz) the signal is translated to 44 MHz where it is then sampled by an A/D converter and demodulated in the “sampled” domain. The high band is translated first to the lower frequency band (608-614 MHz) and processed in the same fashion. The software radio demodulation approach accommodates many different data rates and modulation formats and advantageously allows future radio upgrades to be implemented simply by changing the signal processing program opposed to necessitating an entire analog hardware redesign. The low band transmit signal is a simple frequency source modulated with appropriate information. For the high band transmit signal, the same signal used for the low band transmit signal is mixed with a high frequency signal to produce the desired output. For transmitter operation, the signal processing hardware generates the modulating signal for all different signal formats and data rates. The signal processing hardware outputs a modulating signal input to an oscillator circuit that creates the modulated transmit signal. The modulated signal, for the high band, uses the low band modulator and translates that signal to the proper operating frequency. Since the modulator is the same for both low and high bands it ensures the same signal quality regardless of operation band. Since the radio is a transceiver (two-way link), the design can serve as a master or slave; thus the same design can be employed in the command module 3 as well as in the receiver unit 5.

Data may also be collected in an on-board memory contained within the command module 3. Such onboard or internal memory may be used for temporary storage so that the data may be saved and then downloaded to the tablet computer 6 later, advantageously allowing the subject to wear the movement disorder diagnostic device comprising sensor unit 2 and command module 3 for more prolonged symptom monitoring. Additionally, or in the alternative, the onboard memory may be used to temporarily store the movement data and provide a backup in the event of halted, corrupted, or otherwise incorrect transmission of the data from the movement disorder diagnostic device comprising sensor unit 2 and command unit 3 to the receiving unit 5.

The receiver unit 5 may be, and is preferably integrated into some larger system—for example, it may consist of a wireless receiver, such as a Bluetooth receiver, integrated into a device such as a laptop or tablet computer, a cellular phone, etc.—or it may be a separate device built into an enclosure. However, in the illustrated embodiment, the receiver unit 5 is connected to a tablet computer 6 via one of the USB ports (not shown, in a dongle-style connection that advantageously eliminates a cable), is powered thereby, and comprises a radio frequency transceiver capable of 2-way radio frequency communication with the command module 3. Power regulation and USB-based data transmission protocols may be among any known in the art. The receiver unit 5 may be, in some embodiments, an off-the-shelf Bluetooth USB adapter dongle.

The tablet computer 6 is used to collect data transmitted from the control module 3, allow user inputs to store and track motor performance and therapy device parameter settings, and provide clinicians with real-time symptom quantification feedback. The tablet computer 6 of the illustrated embodiment may be any computing device with a user interface 7, including a smart phone, PDA, laptop computer, desktop computer, iPhone, iPad, or the like. Preferably, the tablet computer 6 is lightweight and portable, allowing for its easy transport within an operating room or other setting where the clinician, physician or technician would utilize the device, and includes a touch screen. In some embodiments, the tablet computer 6 may be equipped with a clip or hanger (not shown) for easy mounting to, for example, an operating room pole. Most preferably, the tablet computer 6 is any mobile device that the clinician, physician or technician can utilize remotely from the subject by receiving any data communicated from the subject's movement disorder diagnostic device, a database, or other sensors or systems used to measure, monitor, or record the subject's movements. The tablet computer 6 allows the clinician, physician or technician to access the data and review and/or analyze it wherever he or she may be, and without requiring the subject to be located in the immediate location or vicinity.

The user interface 7 may be visual, preferably comprising a touch screen, or it may be an audio interface that accepts and transmits spoken commands. In addition, or alternatively, the user interface may be used to provide an automated testing protocol to the subject 1 by providing instructions to the subject 1 on which movement disorder test(s) to perform, and how to perform them. In preferred embodiments, the subject 1 may be instructed on which tests to perform and/or how to perform them on a separate display, not on the tablet's user interface. In such embodiments, the subject 1 and clinician, physician or technician preferably have separate devices with user interfaces such that the subject can receive instructions on movement disorder tests to perform while at home or otherwise remote from the healthcare professionals, and the clinician, physician or technician can access the subject's movement data while remote from the subject. The user interface 7 preferably provides several key components and an overall software wrapper. First, it preferably provides a main menu (not shown) to access all software features including a subject database (not shown), the tuning assistant software, which runs the therapy device parameter settings tuning algorithm, and software for automatically generating clinical reports following tuning sessions. Next, it preferably provides a module to view real-time motion data transmitted by the movement disorder diagnostic device comprising sensor unit 2 and command module 3, helping ensure proper setup and communication prior to clinical therapy device programming. The user interface 7 also preferably communicates with the system registry to store system parameters and clinician preferred settings. Finally, a help menu (not shown) with troubleshooting guides and frequently asked questions is preferably included.

Subject data management is an important aspect of clinically-used embodiments of the present invention. Preferably, the format of the software used with the system is designed for a high volume subject database. Any database known in the art may be used but is preferably one which scales well to accommodate thousands or tens of thousands of subjects. Preferably, the database has fields for subject history, including the subject's surgery dates, a running list of the subject's clinical sessions (past and/or future scheduled), the subject's primary physician, neurologist, medication dosage, etc. Preferably, the subject is also programmed with the ability to import e-mails and other documents into the subject history, and to export a standardized patient information sheet (reporting). Preferably, the database is programmed so as to permit all stored subject information to conform HIPAA guidelines for patient privacy and confidentiality.

A separate programmer device 8 is used in some embodiments by the clinician, physician or technician to remotely program the subject's therapy device, that is, to adjust the therapy device's parameter settings. The separate programmer device 8 may be a separate device from the tablet 6, but more preferably the tablet is capable of providing both functions (see FIG. 2). Whether the separate programmer device 8 is separate or integrated, it communicates with the subject's therapy device (e.g., DBS device), and transmits the desired therapy parameters to the subject's therapy device such that the therapy device operates under the transmitted parameters. Where the programmer device or unit is separate from the tablet, preferably communication between them is by wireless methods as described above. In all embodiments, the programmer device or unit preferably communicates wirelessly with the subject's therapy device.

In addition, or alternatively, the movement disorder diagnostic device comprising the sensor unit 2 and command module 3 may transmit to a server or group of servers constituting a centralized database, such as with cloud computing whereby the data resides on such server or group of servers and can be accessed at the point of testing or some remote location for review by a clinician, physician or technician. Further, the tablet and/or programmer device or unit may also communicate with and transmit data to a centralized database or cloud-based database in order to store the preferred therapy parameters for the particular subject, as well as information regarding the testing and tuning protocols used to arrive at the desired parameters. All the data that is transmitted and stored on a centralized database or cloud-based database is intended to be made accessible to the clinician, physician or technician for later review, for reference when the subject requires additional treatment or tuning, and to be readily available to other clinicians, technicians or physicians who the subject may receive treatment of any variety from and who might need to access the data in order to properly and safely attend to the subject. For example, a subject may reside in one state through the summer months and receive tuning of the therapy device and treatment there, but then may travel to another state for winter months and require similar attention there. The database storage of data allows clinicians, technicians or physicians in both states to readily obtain access to the subject's data and to provide the appropriate care to the subject. In all exchange of information that occurs in the above example and in all other embodiments of the present invention, it is important that information be exchanged securely and in ways that do not improperly disclose a subject's identity. Because of this, in certain preferred embodiments, all personal information of a subject is stored securely at a remote database and is accessible only through a secure network connection wherein both the database and connection protocol are compliant with standards required by the health insurance portability and accountability act (HIPAA). Often, this will require encryption of the data to eliminate the possibility that the data can be read by a third party and many preferred embodiments of the present invention include the use of data encryption.

As indicated in the above example, various embodiments of the present invention involve sending a movement disorder monitoring device home or to another remote location with a subject to be used for movement disorder testing away from a physician's or clinician's place of practice. This step likely occurs after initial programming of the display unit as described above. Once the subject arrives home, the movement disorder monitoring device is placed in the subject's home where it may be powered by either a single or multiple on-board batteries or by another power source in the subject's home such as a standard 120 volt alternating current outlet. Once in the home the display unit may, at intermittent times selected by the programming physician or clinician, alert the subject of the need to perform certain movement disorder evaluation tasks. Alternatively the movement disorder diagnostic device may be worn continuously allowing measurements and recordings to be taken periodically, at scheduled intervals or times, or based on stimulus or measurements that indicate the subject is performing a particular activity, movement or motion, or is experiencing some symptom or side effect of therapy. At these times, the display unit may produce a sound, provide a visual alert on its display screen, or a combination of both as a way to alert the subject. In response to the alert the subject will place at least one sensor on his or her extremity(ies) as instructed by the display unit, if the diagnostic device containing sensors is not already being worn, and will proceed to follow other instructions provided regarding how to properly complete certain tasks used to evaluate the severity of the subject's movement disorder symptoms. Again, alternatively, in some embodiments or circumstances, no particular task may be required, but the device may take measurements and recordings while the subject is performing activities of daily living. In certain embodiments, the subject may be video recorded by the camera of the display unit so that a physician can at a later time verify that the tasks were indeed correctly completed. Preferably, the subject will also answer other questions at this time regarding a subject's self-assessment of his or her symptoms and the subject's adherence to and use of treatments prescribed by the subject's physician or another clinician. Such questions may consist of inquiries related to the subject's perception of the present severity of the subject's symptoms, the subject's most recent dose of pharmaceutically-based treatment, the subject's activity level throughout the day, and other similar pertinent information that is desired to be known by the physician to help better understand a subject's symptoms. As noted above, however, in certain other embodiments, the display unit may not be programmed to alert a subject, but instead may simply be left available for a subject to input data regarding his or her symptoms or to select movement disorder assessment tasks to perform from among various options according to the subject's personal preferences and schedule as well as the subject's own subjective view of the severity of his or her symptoms. The data from the instructed tasks or activities of daily living is then transmitted or communicated to a remote location (e.g., directly to the clinician, physician or technician, or to a database or server) where the clinician, physician or technician can access and analyze the data in order to more accurately provide a next or second level of therapy parameters or setting that more directly addresses the subject's needs

In the embodiment illustrated in FIG. 1, the subject 1 performs a movement disorder test according to instructions. Given the preferred embodiments involve remote monitoring, measuring and quantification of the subject's motor symptoms, the subject is preferably at a remote location, such as at home, when performing the movement disorder test(s) (or while performing the activities of daily living in some embodiments). In such embodiments with remote monitoring and measurement, instructions may be provided by an instructional video clip displayed on a user interface 7 of a tablet computer 6 (which is separate from the tablet computer that is in the possession of the clinician, physician or technician), or on a separate display device (such as a home personal computer, smartphone, or other such device, not shown) advantageously providing the subject with a standardized visual aid to mirror while a test is conducted and data is collected. Alternatively, or in conjunction, the clinician, physician or technician may communicate with the subject via teleconference, or more preferably, video conference and thus provide instructions to the subject live. Video conferencing is preferred over teleconferencing to allow the clinician, physician or technician the ability to monitor and observe the subject as her or she performs the test(s) in order to ensure they are performed correctly. Such a system implemented in software and provided through user interface 7 ensures the same clinical examination protocol is used during subsequent test or programming sessions either in-clinic or in a non-clinical setting, advantageously allowing clinicians to more repeatedly and objectively track symptoms and assuring inter-subject data correspondence. In one embodiment, testing includes (or may be limited to) three types of tremor tasks (resting, postural, and kinetic) and three types of bradykinesia tasks (finger tapping, hand grasps, and pronation/supination). Either alternatively or in addition, testing may include various gait/balance tasks, lower extremity bradykinesia tasks, or other similar tasks as well. The movement disorder diagnostic device, or more specifically the sensor unit 2 of the diagnostic device, collects data, which is sent to command module 3 for transmission via radio link to a receiver unit 5. The processor of the tablet computer 6 processes the movement data to extract kinematic features, which are then fed into a trained scoring algorithm implemented as a software algorithm in the tablet computer 6. The trained scoring algorithm may output a score, which may then optionally be displayed on the user interface 7. A tuning algorithm of tablet computer 6 then computes suggested therapy device parameters or settings based at least in part upon the current therapy device parameter settings and the collected movement data and/or the quantified score computed therefrom. An exemplary tuning algorithm for computing the suggested therapy device parameter settings is illustrated in FIG. 3. In many embodiments, the various algorithms utilized are able to analyze the measured and quantified movement data in correlation to the therapy parameters or settings being provided, determine if those parameters or settings or causing side effects to occur, and be able to anticipate similar parameters or settings that might cause the same side effect to occur. In such embodiments, the algorithm would then know to avoid the parameters or setting that are likely to cause the side effect, and thus avoid including them in the provided set or group of parameters and settings.

Once suggested parameter settings adjustments are computed by the tuning algorithm(s), the adjustments or new parameters or settings may optionally be displayed on the user interface 7. The tablet computer 6 may communicate directly with the programmer device or unit 8, wired or wirelessly, to adjust the parameter settings. The therapy device may be reprogrammed wired or wirelessly, and typical implanted therapy devices are enabled with means of wireless transcutaneous reprogramming. A tuning algorithm of tablet computer 6 then computes suggested therapy device parameters or settings based at least in part upon the current therapy device parameter settings and the collected movement data and/or the quantified score computed therefrom. One example of a tuning algorithm for computing the suggested therapy device parameter settings is illustrated in FIG. 3. In many embodiments, the various algorithms utilized are able to analyze the measured and quantified movement data in correlation to the therapy parameters or settings being provided, determine if those parameters or settings or causing side effects to occur, and be able to anticipate similar parameters or settings that might cause the same side effect to occur. In such embodiments, the algorithm would then know to avoid the parameters or setting that are likely to cause the side effect, and thus avoid including them in the provided set or group of parameters and settings.

Once suggested parameter settings adjustments are computed by the tuning algorithm(s), the adjustments or new parameters or settings may optionally be displayed on the user interface 7. The tablet computer 6 may communicate directly with the programmer device or unit 8, wired or wirelessly, to adjust the parameter settings. The therapy device may be reprogrammed wired or wirelessly, and typical implanted therapy devices are enabled with means of wireless transcutaneous reprogramming.

The alternate embodiment of the invention depicted in FIG. 2 advantageously combines the receiver unit 5, the tablet computer 6, the user interface 7, and the separate programming device 8 into a programmer unit 9 having improved user interface 10, which is preferably a touch-screen interface. Preferably, the subject 1, and the clinician are located remotely from each other. The command module 3 of the movement disorder diagnostic device transmits movement data acquired by the sensor unit 2 of the movement disorder diagnostic device, as described above, to the programmer unit 9, where the movement data is analyzed and parameter settings adjustments are computed. Alternatively, or in conjunction with transmitting the data directly to the programmer unit, the data may be transmitted alternatively or simultaneously to a remote location, which may be a database or server (not shown), from where the clinician, physician or technician may then access the data immediately or at a later time for review and/or analysis. The parameter settings may then automatically updated, with the programmer unit 9 remotely interfacing with the therapy device (not shown) directly to communicate the updated parameters or settings to the therapy device reprogram the therapy device's parameter settings.

Preferably, the touch screens of tablet computer 6 and programmer unit 9 permit a user to interact with the user interface 7 or the improved user interface 10 using a large sterile stylus (not shown), or the user's finger.

In alternate embodiments of the invention, quantification of movement disorder symptoms may be performed using a different form of movement measuring apparatus. In one such example, a webcam built into the tablet computer 6 or programmer unit 9, or a video camera or set of multiple cameras connected thereto, view the subject 1 performing the motion disorder test and feed video data into the tablet computer 6 or programmer unit 9 where, for example, machine vision algorithms measure the motion of the limbs of the subject with respect to time according to any method known in the art. Such a method may consist, for example, in determining marker points along the limb of the subject in order to gauge relative motion, and such a method may be assisted by applying more visible markers (not shown) on various points on the limb of a subject 1, such as is common with motion capture technology. In such case, the need for the movement disorder diagnostic device comprising sensor unit 2, with its accelerometers and gyroscopes, and command module 3, may be obviated.

In one embodiment, an initial programming session will be carried out when the device is first provided to the subject according to a protocol comprising the following steps. The clinician will assess all motor task baseline scores. The system or the clinician can remotely check electrode impedance for wire damage. This can be done be receiving periodic signals corresponding to electrode impedance, though more preferably (either alternatively or in conjunction with periodic signals) allows the clinician to interrogate the device remotely and obtain necessary measurements and signals to determine or relate the electrode impedance to the clinician at his or her remote location. The clinician will record medication dosages, which information preferably includes information relevant to the subject's present level of medication, such as time and dosage of last medication administration. The clinician will select programming motor tasks, and in conjunction with the programmer device or unit 8, 9, the subject will repeat the series of motor tasks for each stimulation setting. The DBS settings or parameters and corresponding scores for each chosen motor task will then be entered preferably remotely and automatically by the system, and where the clinician has the ability to switch between tasks for entering data and selecting which DBS parameters are fixed: frequency, current, pulse width, waveform type, contact setup (mono, bi, tripolar), and the like, and the system communicates those parameters or settings to the therapy device remotely without direct clinician interaction after selection. Finally, the tuning algorithm(s) will assess all motor tasks post-programming, and the clinician will have the option of reviewing, and possibly editing the resultant parameters or settings. Preferably, for the clinician's optional review and editing, the system will provide the ability to enter DBS settings or parameters and scores completely either with a finger or stylus on a touch screen, and/or with a mouse and/or with a keypad or keyboard using the tab key to switch between data input fields. Preferably, the system provides three data input modes for clinician review and editing: (1) enter stimulation and score information, click update, enter next measurement; (2) enter information and display updated tuning map; (3) use stylus/finger/mouse to click on the tuning map for the appropriate measurement, with a new input box appearing to enter score/side effects/notes. Optionally, the initial programming session may be performed without direct interaction with the tuning map, but rather where the algorithm(s) populate the tuning map, and the clinician, physician, or technician engages the optional review function to view the tuning map and/or the parameters or settings chosen by the algorithm(s) in order to approve or deny them based on the subject's needs and the results from those therapy settings.

It is advantageous in some embodiments for the system to provide, or to permit the clinician performing the programming session to enter a large number of variables in order to provide a complete assessment of the DBS tuning. The following is a representative list of information that may be entered in each programming session: (1) general subject information, including patient ID, whether the subject's DBS implant is unilateral or bilateral, implant electrode site location and side; (2) motor evaluations performed during tuning, including (a) tremor: rest, postural, kinetic, (b) bradykinesia: finger tap, hand grasp, pronate/supinate, (c) rigidity: elbow/knee, head/neck, (d) leg agility: heel tapping, (e) rising from chair: with arms crossed, (f) posture, (g) gait: walking quality, (h) postural stability: pull back; (3) motor scores, in the form of integer scoring from 0 (no severity) to 4 (extremely debilitating); (4) DBS settings or parameters, including, but not limited to (a) contact: cathode/anode, monopolar, bipolar, tripolar, multiple channels with fractionalized control, waveforms, current steering, different waveforms, interleaving multiple waveforms, etc., (b) stimulation parameters including amplitude (in volts), frequency (in Hz), current (in amps), pulse width (in microseconds), the type of waveform of the stimulation impulse, (c) side effects and/or capsule effects, including (i) motor effects, such as worsening of symptoms, dyskinesias, facial pulling, (ii) non-motor effects, such as blurry vision, soft or slurred speech, sweating, headache, tingling (transient/non-transient), fatigue, sense of euphoria, paresthesia, and/or (iii) new or atypical side effects and update list of notable effects.

FIG. 3 shows merely one optional example of a tuning algorithm used for computing suggested parameter settings adjustments. This basic tuning algorithm utilizes symptom severity data, detected stimulation induced dyskinesias (SID), and clinical inputs such as clinician defined improvement percentage (CDI %) to compute suggested stimulation parameter settings. Based on the typical clinical description, several constraints reduce the number of degrees of freedom in the tuning algorithm. During DBS programming, the clinician or the algorithm may utilize any subset of the motor task mentioned previously to evaluate motor performance. The average tremor score (ATS) is computed for the set of tremor tasks and the average bradykinesia score (ABS) is computed for the set of bradykinesia tasks utilized for a given iteration. This reduces the number of symptom severity outputs from a maximum of three to one for each symptom. Dyskinesia is either “on” or “off.”

Recording symptom severity before the therapeutic device is turned on obtains baseline. In the case of DBS adjustment, the best monopolar electrode contact is determined by finding the contact that provides the largest therapeutic width, i.e., the largest change in supplied voltage from when a clinical benefit is noticed to when side effects occur. This is accomplished by fixing stimulation pulse width to initial settings, for example 60 μs, frequency to 130 Hz, selecting one contact, and then stepping the voltage amplitude in small increments of approximately 0.2 V. The procedure is repeated for each contact. The contact that provides the largest therapeutic width is selected. With the pulse width (60 μs) and frequency (130 Hz) set to typical values, the clinician or the algorithm then sets the amplitude to the lowest voltage that provides a significant decrease in symptoms. If a satisfactory result is not achieved, pulse width or frequency may also be increased. This can be a time consuming iterative process that must be completed several times over the first few months as microlesioning heals and requires a compensatory increase in stimulation amplitude to maintain clinical benefit. In various embodiments, the invention includes a sensitive tool, implemented in software and accessed through user interface 7 or improved user interface 10 to detect the instant of clinical benefit as voltage amplitude is increased and the instant any stimulation induced dyskinesias are detected. Use of the invention as a sensitive measure of clinical benefit onset and side effect occurrence advantageously ensures the contact with the greatest therapeutic width is selected.

Once the contact width is selected, the initial parameter settings adjustment iteration may be completed with literature-defined settings of 60 μs and 130 Hz stimulation. Amplitude is set to 0.2 V initially, and then modified by the clinician or algorithm in subsequent iterations. After each stimulation parameter change, the clinician may use the user interface 7 or the improved user interface 10 to guide the subject through motor tasks, or, in some preferred embodiments, the device will in an automated fashion provide instructions to the subject via a display to provide such guidance. The tuning algorithm output provides a suggested parameter direction output after each motor task evaluation by utilizing the movement disorder quantification algorithm. The invention thereby maximizes clinical benefit by minimizing tremor and bradykinesia, minimizes adverse effects of stimulation-induced dyskinesias, and minimizes current consumption to maximize battery life. Thus, one objective function is to minimize the sum of average tremor score (ATS) and average bradykinesia score (ABS), known as the summed motor score (SMS) 11. This objective is achieved in the tuning algorithm by continuing to increase stimulation in the same direction as long as SMS is decreasing. A higher SMS corresponds to worse motor symptoms. A second constraint is that stimulation induced dyskinesias (SID) should not occur. If they are detected, the direction of the parameter change is reversed. Another system constraint is the minimization current consumption. This is accomplished by allowing a clinician defined improvement percentage (CDI %) 12 and considering any changes of less than 5% in SMS to be insignificant. When these conditions are met, the current parameter level is maintained 13 due to the SMS goal being achieved and with consideration given to diminishing clinical returns, in order to maximize battery life. Once optimized amplitude has been achieved or reaches 3.6 V, the clinician may adjust pulse width or frequency utilizing the same algorithm. The chances of the feedback system settling into local minimums are reduced by ensuring several of the settings are set at clinically accepted levels for the initial iteration and making only moderate adjustments as required.

While DBS programming frequently entails stepping through small incremental changes, this process can be wastefully time-consuming if the motor symptom response of the subject indicates larger changes are required. The implementation of an artificial neural network 14 to output suggested stimulation parameters minimizes programming iterations to reduce surgical and outpatient tuning session time.

Preferably, the artificial neural network 14 used in the tuning algorithm used by some embodiments of the present invention is trained with recorded clinician-made stimulator parameter changes in response to motor symptom severity changes during stimulator programming to minimize required iterations while still utilizing objective symptom severity measures to optimize performance. In this way, the algorithm takes clinician experience into account. Experienced clinicians are generally successful in quickly reducing the number of potentially successful parameter settings for tuning DBS systems. An expert clinician is capable of recognizing severe motor symptoms and modifying a parameter by a larger magnitude, then when the symptom is less and only fine-tuning is required. The present invention is therefore capable of quantitatively detecting motor symptom severity and suggesting a parameter change that approximates or mirrors the parameter change that would be made by an expert clinician.

Artificial neural network 14 may be implemented, for example, with the MATLAB Neural Network Toolbox offline using resilient backpropagation batch training. Inputs to the neural network may include current and previous stimulation settings 15 and motor responses.

FIG. 4 illustrates a two-layer network structure consisting of one hidden layer 17 with four neurons using “tansig” transfer functions and one output layer 18. As neural networks may fall into local minimums when being trained, each network is preferably trained three times with randomized initial weights and biases and the best training results are selected. Additionally, early stopping improves network generalization. Data is separated into training and generalization sets to ensure trained networks produce accurate results both when the training set is reapplied and also when it is generalized to new data. Preferably, training data is collected from multiple patients. To ensure that the system generalizes to new patients, network generalization can be tested by training the system using a jackknife “one left out” method. Using such a method, the neural network is trained using data from, for example, only nine of ten subjects. Data from the nine subjects in the training set is then reapplied to the trained network to ensure good correlations while data from the “left out” subject is used to test generalization. The method is repeated, leaving out each subject one time. For each training and generalization set, both the mean squared error (MSE) and R-squared values between the clinician-made stimulator parameter changes and those output by the system for each stimulation parameter are calculated. The MSE and R-squared for all training and generalization sets are averaged. Preferably, the system achieves normalized MSE values of less than 10% and R-squared values of greater than 0.8 to show substantial agreement between system-suggested simulation parameter changes and clinician-made stimulation parameter changes.

Preferably, separate data sets and acquired, and separate neural networks are trained, for the surgical and outpatient scenarios. Preferably, the data used to train the algorithm averages the experience of multiple expert clinician programmers.

Preferably, the tuning algorithm comprises a neural network as illustrated in FIG. 3, but it might instead or in addition comprise one or more of adaptive continuous learning algorithms, linear quadratic Gaussian control, Kalman filtering, and model predictive control.

When the tablet computer 6 is connected to the Internet or similar communications network, wired or wirelessly, it may therefore transmit subject data to remote systems, allowing general practitioners to conduct DBS programming remotely, minimizing travel for a subject 1 who lives far from a DBS implantation center or suitable programming clinic, so long as the subject 1 is equipped with the movement disorder diagnostic device comprising sensor unit 2 and command module 3 and means of programming and/or making parameter settings adjustments to his or her therapy device (including DBS implant).

FIG. 5 depicts a series specific display pages corresponding to reporting score provided for optional or periodic review by a clinician, physician or technician in various embodiments of the present invention. These examples of methods of reporting scores with visual displays associated with each display stage of the test process are merely exemplary, and many variations of the display method, as well as the labeling of the display, are envisioned. One example of displaying a score for optional or periodic review is the expandable menu view 19, where the user (i.e., clinician, physician, or subject performing self-testing away from the clinician) is presented with a list of the different types of movement disorders or movement disorder symptoms, which may or may not have been measured in a given test. In the portrayed example of this expandable menu view 19, the movement disorder symptoms that may be selected include tremor, bradykinesia, rigidity, gait and/or balance disturbances, and the like. The user is then given the option of expanding the results for each of those movement disorders or movement disorder symptoms through a series of levels (i.e., hand then to left or right), in order to view the score that was determined for each particular disorder or symptom in the indicated portion of the subject's body. By way of clarification and example, the subject shown in menu view 19 received a score of 1 during rest for the symptom of tremor in the left hand, and a 2 for the pronation/supination task for bradykinesia in the left hand.

Another optional review display method, which may be independent or used in conjunction with the expandable menu view is the tuning map 20. A tuning map 20 is generated for each task that the subject is directed to perform, symptom, or side effect and depicts the severity of the symptoms measured in each sensor that is used for the given task. Each task, symptom or side effect that is performed may be represented on a different tab (i.e., tab A, tab B, tab C, where the lettered tabs in FIG. 5 either correspond to a task, symptom or side effect). Alternatively, the tab letters may be replaced by other labels or indicators, or even the name, or abbreviation thereof, of the task, symptom or side effect. Further alternatively or in addition, the different tabs may represent combinations of tasks, symptoms (e.g., averaged results of multiple symptoms), and/or combinations of side effects. In this particular embodiment, the amplitude 21 at which the test was performed is measured in volts and indicated on one vertical axis of the tuning map 20. Also in this particular embodiment, the contact being used to provide therapy or stimulation is indicated on the horizontal axis 23. However, in many embodiments, the axes may represent any other test parameter or setting used, and in some preferred embodiments, one of the axes (typically the horizontal) may represent different groupings (see FIG. 6) of test therapy settings or parameters. In such embodiments, for example, the axis would provide a representation of a group (e.g., 1 representing group 1), which would comprise a predetermined set of therapy parameters or settings being tested, where the grouping may include variations of any of the above described therapy parameters or settings, such as contact, current, frequency, waveform, polarity, pulse width, and the like, as well as combinations thereof. Some embodiments further allow the condition to use varying or comparative settings within the groupings. For example, it may be decided to combine the settings in such a manner to provide weighted scores where one symptom, task, or side effect is given a greater weight than another, but they are combined to create a single weighted map. Similarly, therapy settings or parameters may be gradually increased or decreased as symptoms are continuously measured, rather than providing measurements or assessments at discrete amplitude levels. This allows a greater degree of freedom and versatility in defining the test settings instead of being limited to one or two test parameters per test. Such groupings decrease the amount of testing time required for tuning, and thus reduce cost to the subject or insurance company, as well as opportunity cost of the clinician's time spent with a single subject. The calculated or estimated score 22 is depicted on a vertical axis of the tuning map 20 as well, and is indicated as various cross-hatch patterns. Alternatively, the score may be represented by colors, shapes, or any other indicator. Each individual box that is shown represents a test performed 24. Preferably, the tuning map 20 is shown on a color display (not shown) for optional review and the severity of the symptom is indicated by color. In this drawing, the colors are represented by different types of shading or cross-hatching rather than by the preferred color. Each column in the tuning map 20 represents a different contact on the DBS probe. Therefore, each individual test box 24 depicts the results of performing a task while administering DBS at a prescribed voltage amplitude 21 and provides both a severity of the symptom that was detected or measured by virtue of the color (represented by the cross-hatching), which also correlates to a given motor score. Additionally, each individual test box 24 may be selected, for example by pressing it on a touch-screen device, as representing by the test boxes 24 which are outlined in black. When a test box 24 is selected, the user is able to see a detailed view (not shown) of the statistics and parameters of the test corresponding to that box. In many embodiments, the clinician, physician or technician may be able to add notations to the different parameter or setting groupings, or even to the individual test scores. Such notations may include commentary or other notes regarding the efficacy of the given parameter grouping or score, of side effects that occur, or any other notations the clinician, physician or technician deem necessary.

A variable window 25 may display on the unit as well that allows the user to input various conditions that have an effect on the test and test results. These variables are calculated into the test results and help to give a more accurate calculated symptom score.

Other methods of displaying data corresponding to test therapy settings or parameters and results may also be envisioned and are considered for use with the present invention.

FIG. 6 portrays one example of the optional or periodic review tuning maps 20 or other visual display tool or method for a particular embodiment with amplitude 21 on the vertical axis and groupings of parameters or settings 23 on the horizontal axis, in greater detail. Each task performed, symptom or side effect is represented again by a separate tab with its own tuning map 20. Though the tabs are labeled as A, B, C and A+B in the figure, in many preferred embodiments the tab may be labeled with a number, the name of the task, symptom or side effect it represents, an abbreviation thereof, or some other label indicating to the clinician, physician or technician what information is represented in the given tab. The amplitude 21 of the voltage at which the test was performed is tracked along one vertical axis of the map 20 for each grouping of parameters or settings 23 on the DBS lead (for this particular depicted embodiment), while the severity of the symptom detected or measured is displayed as a score 22 and correlated to an indicator (e.g., cross-hatching pattern, color, or the like) of each individual test result box 24. Again, in many preferred embodiments, rather than the labeling the groupings of parameters or settings 23 used to provide stimulation with letters (e.g., W, X, Y, Z in the figure), they may instead be labeled by a grouping number, grouping name, or any other labeling scheme or plan, which indicates to the clinician, physician, or technician which grouping of settings or parameters is represented. Preferably, the groupings are cross-referenced within the software and/or GUI such that a user, clinician, physician or technician may readily and easily be able to see what parameters or settings correspond to the chosen grouping label. The right side 26 of FIG. 6 portrays a new tab 27, which represents the combination of tabs A and B. This combination tab 27 represents the combination of the tuning maps for tasks A and B, and the combination can be of any mathematical variety such as averaging, weighted averaging, or the like.

The combination 27 is a result of the user selecting those two tuning maps to be combined together and optimized in some mathematical way (e.g., averaging) in order to show the results of how the scores for each task combine in order to optimize the DBS level for treating the subject. In other words, the goal is to minimize the voltage at which the DBS is to be supplied while simultaneously minimizing the severity of the subject's symptoms and/or side effects. Combining the tuning maps for each task allows the user to see a resulting score and select the DBS test parameters, which are as close to optimal as possible. In a preferred embodiment, the system would be designed to be a closed-loop system, (i.e., for an implanted home-diagnostic and therapeutic device), which would not require extensive, or any, user input, but would perform the optimization automatically.

FIG. 7 depicts the screening process for determining whether a subject is a good and viable candidate for DBS therapy. When a subject begins to experience side effects 35 from medication he or she is taking to treat the symptoms of a movement disorder, the subject or his or her clinician may begin to consider new treatment methods other than simply relying on medication. The clinician may then have the patient undergo a home screening assessment 36 to perform monitoring and recording of the subject's symptoms and test results (motor and cognitive tests) with a system for monitoring and recording those results. Next, preferably the system utilizes predictive tuning algorithms 37 to analyze the many variables and test results in order to make a determination as to whether the subject would benefit from DBS therapy 38. If the determination is that the subject would benefit, then the clinician and subject may decide to undergo DBS surgery and therapy 38. However, if DBS is not a viable option for the subject, then alternative therapies 39 should be considered.

FIG. 8 depicts a method of the present invention utilizing an automated and intelligent DBS (or other therapy) training system. As the subject 40 performs motor and cognitive tests, as instructed via a display 42 (automated), while wearing the movement disorder diagnostic device 41, the system records and analyzes the results of those tests in light of the many variables. The system then populates a tuning map 45, in the background, to show the subject's response to the tested DBS (or other therapy) parameters for each symptom while substantially simultaneously entering the same data into a tuning algorithm(s). As noted herein, the axes 43 of the optional or periodic review tuning map 45 may represent a single test variable, or may represent a grouping of therapy settings or parameters that are used while the subject 40 conducts a movement disorder test(s). The tuning map 45 is populated by scores 47 representing the severity of the subject's 40 symptom or side effect, or some other metric being measured, and the same data is entered into the tuning algorithm(s). The tuning map 45 also helps to map the side effect regions, as well as therapeutic and non-therapeutic regions. A clinician, technician or physician then determines, based on the test results, a set of therapy settings or parameters that are then entered into the subject's therapy (e.g., DBS) device. Alternatively, the system may utilize the tuning map 45 to suggest DBS parameters that will activate the necessary therapeutic regions, avoid the side effect regions, and optimize the life of the device (i.e., battery life, etc.). The parameters are then entered into the subject's therapy device (not shown) for further testing or for delivering treatment and therapy to the subject.

FIGS. 9A and 9B illustrate the difference between (A) monopolar and (B) bipolar DBS lead configurations and the ability to activate desired brain areas while avoiding others by shaping the electrical stimulation field. While only mono- and bipolar configurations are depicted, other configurations are envisioned for use with the present invention as well. A monopolar (A) configuration provides electrical stimulation in all directions from the given contact, and that stimulation dissipates as it radiates out from the contact. Bipolar (B) configurations simultaneously provide electrical stimulation from 2 contacts, and depending on the parameters used, can thus alter the shape of the electrical stimulation field. It follows that additional contacts on a single lead can enable more complex shaping of the stimulation field by selecting and providing stimulation from any combination of contacts on a single lead together, such as fractional control of the leads, segmented leads, steering of the electrical current, and the like. FIG. 9(A) shows a monopolar configuration, which provides electrical stimulation in all directions. Thus, with a monopolar configuration, the stimulation provided activates the desired portion 56, which corresponds to the area of the subject's brain which causes occurrence of a movement disorder symptom, but also activates a side effect region 55, which causes a side effect to occur. However, with a bipolar configuration, as in FIG. 9(B), the electrical field may be shaped in such a manner so as to activate the desired region 56 corresponding to a symptom and thus treat that symptom, while avoiding the side effect region 55 and thus avoid causing the side effect to occur.

Preferably, in many embodiments, the electrode leads are adapted to not only provide or emit electrical stimulation, but also to detect, acquire or measure signals from the subject's body.

In such embodiments, each electrode lead may comprise separate contacts for providing electrical stimulation and separate contacts for detecting, acquiring or measuring (sensing) signals from the subject's body. Preferably, the same contacts on each lead are capable of performing both the emission and sensing functions. Multi-polar leads (leads comprising two or more contacts—e.g., FIG. 9B) are preferable in order to enable substantially simultaneous acquisition or measurement of signals from the subject concurrent with providing electrical stimulation to the subject as needed. While it may be possible to provide stimulation to the subject and sense physiological or electrophysiological signals from the subject substantially simultaneously from a single contact, it may be preferable to only employ each contact for a single function at any given time in order to minimize perturbation of either the stimulation provided or the signal(s) acquired. Given that the stimulation being provided is a known quantity provided under known conditions, any acquired physiological or electrophysiological signals can easily be preprocessed or processed to remove any noise caused by provided electrical stimulation, and thus the simultaneously acquired physiological or electrophysiological signals will effectively be unperturbed by noise or artifacts—even if the same contact is used for both functions substantially simultaneously. The dual-mode capability of the DBS leads/contacts in such embodiments is particularly useful in monitoring the subject's brain function locally as opposed to from external sensors or even implanted sensors that are located on the surface of the subject's brain (e.g., electrocorticography sensors), both of which are able to provide general brain activity monitoring from the surface, but generally cannot resolve beyond surface location, and certainly cannot pinpoint at the neuron bundle-level or individual neuron level. Sensing with the implanted DBS leads allows for much higher resolution brain mapping by acquiring signals from the subject from multiple locations and depths, allowing for much more accurate identification of brain activity throughout the entire brain, not just from the surface. Further, the dual-mode functionality of the leads and/or contacts allows for such accurate sensing and identification of brain activity of the subject in response to provided electrical stimulation as it is applied to the particular area of the brain targeted so that immediate and specific feedback can be obtained directly from the stimulated neurons and brain structures.

FIG. 10 depicts an exemplary embodiment of a device that can be carried, attached to, or otherwise readily used by the subject. The device 70 can be any variety of interface devices, such as a cellular or smart telephone, PDA, tablet, or the like. In the particular embodiment depicted, the subject can view the device 70 and view, determine and select the type of activity, action or task 78 he or she is about to perform or is performing. The activity list 78 depicts any number of activities, actions or tasks 78, such as activities of daily living that the subject may perform at various points in his or her day. When, for example, the subject knows he or she is going to go for a walk, the subject can merely select the walking activity, and the device 70 would then communicate a set of predetermined therapy settings or parameters to program the subject's therapy device (such as an implanted DBS device). The therapy device would then operate under the newly programmed therapy settings or parameters. The predetermined settings or parameters may be determined and programmed into the device by a clinician, physician or technician before the device is provided to the subject. The initial and subsequent parameters or settings may also be determined by a clinician, physician or technician and transmitted or communicated remotely to the subject's DBS or therapy device. Also alternatively, the system may in an automated fashion and intelligently determine preferred settings or parameters based on the activity, action or task being performed, and the measurements acquired while performing said activity, action or task 78. This automated setting or parameter determination may initially be based on past performances of the elected activity, action or task 78, but real-time measurements during each performance of the activity, action or task 78 may allow for real-time setting or parameter needs to be determined and communicated from the device 70 to the therapy device. Thus, whenever the subject selects a desired activity, action or task 78, the device would automatically reprogram the subject's therapy device to provide the appropriate settings or parameters to most effectively allow the subject to perform the elected activity, action or task 78.

Particularly in the intelligent, automated parameter or setting determination embodiments, the subject may be given the option to define a new activity, action or task 82. Such capability would allow the subject to perform a new activity, action or task that he or she has not done before, or to create a new level of a previously defined activity, action or task (such as creating a different activity, action or task 78 in the activity list 74 for mowing the lawn and gardening, as opposed to just yard work, as depicted by way of example in the figure). It may be preferable, for some subjects, to only allow a clinician, physician or technician to define new activities, actions or tasks in which case this feature would either be disabled or removed from the interface or device 70. In any event, the automated, intelligent programming systems would allow the device 70 to take real-time measurements of the subject's movements, analyze the movement data, and determine the best therapy parameters or settings for maximizing the subject's ability to perform the activity, action or task. This maximization of the subject's ability to perform the activity, action or task may correspond to a minimization of symptoms, a minimization of side effects, a combination thereof, or any other combination of possible desired results or outcomes described herein as they pertain to the particularly elected activity, action or task.

Optionally, the device 70 may provide the ability to view and review the settings for an elected activity, action or task 76. The subject or a clinician, physician or technician may simply select a button 80 corresponding to the settings for a particular activity, action or task, and the therapy parameters or settings would be displayed for review. Depending on the embodiment or the person reviewing the settings, this option may also provide the ability to manually edit the prescribed settings for a particular activity, action or task. Preferably, the ability to edit the parameters or settings is available only to a clinician, physician or technician. The subject may be able to view the settings in order to take notes or to discuss the settings with the clinician, physician or technician while at a remote location such as at home. In light of the various features of the device that upload and store the parameters and settings in a database, either in real-time or on-demand, the activity settings are similarly reported and stored for remote (in time, location, or both) access.

FIG. 11 depicts a flow chart representing a method embodiment of the present invention for tuning the movement disorder diagnostic device with new therapy parameters or settings. This particular embodiment is envisioned to be performed in an automated fashion, by an algorithm(s), with little or no interaction required with a clinician, physician or technician, and where the subject is located remotely from a clinician, physician or technician, such as at home. First, a subject who has a therapy device, such as a DBS therapy device, wears a movement disorder diagnostic device 90. The therapy device preferably has a first level of therapy parameters or settings already programmed or entered into the device. The movement disorder diagnostic device is as described herein, but preferably comprises at least one physiological or movement sensor for measuring the subject's external body motion, or some other physiological signal of the subject, where the sensor(s) has a signal related to the subject's motion or other physiological signal. Preferably, the movement disorder diagnostic device is a single unit, though may be multiple units (e.g., separate sensor unit and command/transceiver module), and is adapted to be worn or attached to a portion of the subject's body such that the sensor(s) of the movement disorder diagnostic device are able to measure the movement of that particular portion of the subject's body.

Once the subject is wearing the movement disorder diagnostic device 90, the diagnostic device then identifies or determines what, if any, activity, movement or motion the subject is performing 92. This identification or determination is made based on the signal from the at least one physiological or movement sensor of the diagnostic device. The identification or determination may be made by the subject who may manually input the new activity, movement or motion into the device, by a clinician either local or remote to the subject, who may interpret the signals from the at least one sensor and identify or determine the activity, movement or motion, or more preferably, automatically by the device. Automatic identification or determination of the subject's activity, movement, or motion is preferably performed by a processor contained within the device which may include a separate algorithm for activity identification or determination, or the measurement and quantification algorithms may be able to perform this function. Preferably, even where the subject or a clinician, physician or technician identifies or determines and inputs the activity, movement or motion, the device uses the signal from the at least one sensor to estimate or predict the activity, movement or motion, and allows the particular user to confirm the identified or determined activity, movement or motion or to override the suggested identification and input another. The system may provide the user with several options to select from as well, all based on the signals from the at least one sensor.

Once the subject's activity, movement or motion has been identified or determined 92, the next step is to identify or determine any motor symptoms of a movement disorder or side effects of a treatment 94 that the subject is experiencing as experienced while performing the identified or determined movement, motion or activity. The identification or determination of symptoms or side effects 94 is similarly capable of being performed by either a human user or automatically, or combination thereof. Much like the activity identification 92 above, the subject or a clinician may input into the system any known symptoms or side effects. Preferably though, the system automatically identifies or determines the symptoms or side effects based at least in part on the signal from the at least one sensor, and at least in part on the identified or determined activity, movement or motion. Again, a separate symptom and side effect identification algorithm may utilize the signal from the sensor and the identified activity to identify or determine what symptoms and/or side effects the subject is experiencing or has experienced. This algorithm may then present options to the subject or to a clinician, again either local or remote, for confirmation or to be rejected.

Next, the step of measuring and quantifying the identified or determined motor symptoms or side effects 96 of the subject based at least in part on the signal of the at least one sensor(s). The movement disorder diagnostic device uses the signal of the at least one sensor(s) to provide an objective measurement and quantification of the severity of the subject's motor symptoms or side effects while the subject performs the identified or determined activity, movement or motion. The measured and quantified motor symptoms or side effects may include specific movement disorder symptoms, side effects from medication and/or therapy, or combinations thereof. The trained scoring algorithms of the movement disorder diagnostic device perform various measurements and calculations to provide this objective quantification of the subject's motor symptoms.

Once the subject's motor symptoms or side effects have been measured and quantified 96, data corresponding to these measured and quantified motor symptoms is entered into a processor comprising an algorithm(s) 98 for automated analysis. Typically, the data corresponding to the measured and quantified motor symptoms is an objective score, as described herein, but may be represented in numerous ways and means. Preferably, the quantified movement data is entered 98 directly and automatically into the processor and into the determination algorithm(s) without the need for any manual human intervention, such as keying in the data.

The processor and its algorithm(s) then analyze the measured and quantified movement data and calculate a second level of therapy parameters or settings 100. This second level of parameters or settings preferably corresponds to a mode of therapy or treatment that addresses the subject's needs as determined based on the identified or determined activity and symptoms or side effects, and the measured and quantified motor symptom data, as well as other data, goals, or objectives. In other words, if the system determines that, for example, the subject is experiencing a very strong tremor while driving, such determination being made as a result of the identification of the subject's activity and symptoms as well as measurement of the subject's movement and quantifying the severity of the tremor, the processor and algorithm would provide a second level of parameters or settings 100 that would reduce or minimize the tremor the subject is experiencing. As noted, the calculation of a second level of parameters or settings may be based on any number of constraints or desired results for the subject, not solely the immediate symptom or side effect the subject is experiencing. For example, if the subject's main concern is reducing or minimizing symptom occurrence and or severity, the processor and its algorithms will take this desired goal into account when calculating the second level of parameters or settings. Similarly, the calculation of settings or parameters may be based on a desired reduction or minimization of side effects from medication or the therapy. Also, the calculation may be made to balance multiple desired results, such as if a slightly higher rate of occurrence of symptoms is acceptable to the subject in exchange for a minimization of side effects. Other examples of desired results that may be used to determine the second level of therapy parameters or settings for all embodiments include, but are not limited to, a therapeutic window (in terms of time or some other factor) in which the subject most positively responds to therapy, battery life, and other such constraints that might be considered in terms of optimizing the therapy parameters or settings. In any determination, the subject and the clinician, physician or technician decide what the initial desired result is, and these desired results, goals, or constraints are programmed into the processor and its algorithm(s) for the decision making process. In some embodiments, the desired result, goal or constraints may be edited, either by a clinician, physician or technician, or by the subject, in order to allow the algorithm to most accurately analyze the data in light of the optimal treatment for the subject.

Once this second level of parameters or settings has been calculated by the processor and its algorithm(s) 100, the parameters or settings are then transmitted to the subject's therapy device 102 (e.g., DBS device). The therapy device preferably comprises at least one electronic component for receiving such signals, and uses this at least one component to receive a signal(s) comprising the second level of therapy parameters or settings from the diagnostic device. The parameters or settings may additionally transmitted via wireless communication, as described herein, between the subject's therapy device and a remote location such as to a database or server for storage, or directly to a remote clinician physician or technician for optional review. Once the therapy device receives the transmitted 102 second level of therapy parameters or settings, the second level of parameters or settings is then entered in to the subject's therapy device 104 for the device to provide therapy according to those parameters or settings. As such, the new, second level of parameters or settings is programmed into the subject's therapy device, and the device then operates according to those new parameters or settings 104 and provides that newly determined course of therapy or treatment to the subject.

FIG. 12 depicts a flow chart representing a method embodiment of the present invention for tuning the movement disorder diagnostic device with new therapy parameters or settings. This particular embodiment is envisioned to be performed in an automated fashion, by an algorithm(s), with little or no interaction required with a clinician, physician or technician, and where the subject is located remotely from a clinician, physician or technician, such as at home. First, a subject who has a therapy device, such as a DBS therapy device, wears a movement disorder diagnostic device 90. The therapy device preferably has a first level of therapy parameters or settings already programmed or entered into the device. The movement disorder diagnostic device is as described herein, but preferably comprises at least one physiological or movement sensor for measuring the subject's external body motion, or some other physiological signal of the subject, where the sensor(s) has a signal related to the subject's motion or other physiological signal. Preferably, the movement disorder diagnostic device is a single unit, though may be multiple units (e.g., separate sensor unit and command/transceiver module), and is adapted to be worn or attached to a portion of the subject's body such that the sensor(s) of the movement disorder diagnostic device are able to measure the movement of that particular portion of the subject's body.

Once the subject is wearing the movement disorder diagnostic device 90, the diagnostic device then identifies or determines what, if any, activity, movement or motion the subject is performing 92. This identification or determination is made based on the signal from the at least one physiological or movement sensor of the diagnostic device. The identification or determination may be made by the subject who may manually input the new activity, movement or motion into the device, by a clinician either local or remote to the subject, who may interpret the signals from the at least one sensor and identify or determine the activity, movement or motion, or more preferably, automatically by the device. Automatic identification or determination of the subject's activity, movement, or motion is preferably performed by a processor contained within the device which may include a separate algorithm for activity identification or determination, or the measurement and quantification algorithms may be able to perform this function. Preferably, even where the subject or a clinician, physician or technician identifies or determines and inputs the activity, movement or motion, the device uses the signal from the at least one sensor to estimate or predict the activity, movement or motion, and allows the particular user to confirm the identified or determined activity, movement or motion or to override the suggested identification and input another. The system may provide the user with several options to select from as well, all based on the signals from the at least one sensor.

Once the subject's activity, movement or motion has been identified or determined 92, the next step is to identify or determine any motor symptoms of a movement disorder or side effects of a treatment 94 that the subject is experiencing as experienced while performing the identified or determined movement, motion or activity. The identification or determination of symptoms or side effects 94 is similarly capable of being performed by either a human user or automatically, or combination thereof. Much like the activity identification 92 above, the subject or a clinician may input into the system any known symptoms or side effects. Preferably though, the system automatically identifies or determines the symptoms or side effects based at least in part on the signal from the at least one sensor, and at least in part on the identified or determined activity, movement or motion. Again, a separate symptom and side effect identification algorithm may utilize the signal from the sensor and the identified activity to identify or determine what symptoms and/or side effects the subject is experiencing or has experienced. This algorithm may then present options to the subject or to a clinician, again either local or remote, for confirmation or to be rejected.

Next, the step of measuring and quantifying the identified or determined motor symptoms or side effects 96 of the subject based at least in part on the signal of the at least one sensor(s). The movement disorder diagnostic device uses the signal of the at least one sensor(s) to provide an objective measurement and quantification of the severity of the subject's motor symptoms or side effects while the subject performs the identified or determined activity, movement or motion. The measured and quantified motor symptoms or side effects may include specific movement disorder symptoms, side effects from medication and/or therapy, or combinations thereof. The trained scoring algorithms of the movement disorder diagnostic device perform various measurements and calculations to provide this objective quantification of the subject's motor symptoms.

Once the subject's motor symptoms or side effects have been measured and quantified 96, data corresponding to these measured and quantified motor symptoms is entered into a processor comprising an algorithm(s) 98 for automated analysis. Typically, the data corresponding to the measured and quantified motor symptoms is an objective score, as described herein, but may be represented in numerous ways and means. Preferably, the quantified movement data is entered 98 directly and automatically into the processor and into the determination algorithm(s) without the need for any manual human intervention, such as keying in the data.

The processor and its algorithm(s) then analyze the measured and quantified movement data and calculate at least two optional groups of therapy parameters or settings 106. These optional of parameters or settings each preferably correspond to a mode of therapy or treatment that addresses the subject's needs as determined based on the identified or determined activity and symptoms or side effects, and the measured and quantified motor symptom data, as well as other data, goals, or objectives. More specifically, each optional group of parameters or settings preferably corresponds to a different and separate motor symptom or side effect identified above. In other words, if the system determines that, for example, the subject is experiencing a tremor and increased rigidity while sleeping, such determination being made as a result of the identification of the subject's activity and symptoms as well as measurement of the subject's movement and quantifying the severity of the tremor and rigidity, the processor and algorithm would provide at least two optional groups of parameters or settings: at least one that would reduce or minimize the tremor the subject is experiencing, and at least one that would ease or reduce the rigidity. As noted, the calculation of optional groups of parameters or settings may be based on any number of constraints or desired results for the subject, not solely the immediate symptom or side effect the subject is experiencing. For example, if the subject's main concern is reducing or minimizing symptom occurrence and or severity, the processor and its algorithms will take this desired goal into account when calculating the optional groups of parameters or settings. Similarly, the calculation of settings or parameters may be based on a desired reduction or minimization of side effects from medication or the therapy. Also, the calculation may be made to balance multiple desired results, such as if a slightly higher rate of occurrence of symptoms is acceptable to the subject in exchange for a minimization of side effects. Other examples of desired results that may be used to determine the second level of therapy parameters or settings for all embodiments include, but are not limited to, a therapeutic window (in terms of time or some other factor) in which the subject most positively responds to therapy, battery life, and other such constraints that might be considered in terms of optimizing the therapy parameters or settings. In any determination, the subject and the clinician, physician or technician decide what the initial desired result is, and these desired results, goals, or constraints are programmed into the processor and its algorithm(s) for the decision making process. In some embodiments, the desired result, goal or constraints may be edited, either by a clinician, physician or technician, or by the subject, in order to allow the algorithm to most accurately analyze the data in light of the optimal treatment for the subject.

Once the at least two optional groups of therapy parameters or settings has been calculated 106, the next step is to select at least one of the groups 108 to be used by the therapy device. In the particularly depicted embodiment, the subject selects at least one of the groups to be used; however, the selection of at least one optional group may alternatively be made by a clinician, physician, technician or other user, or automatically by the system. Where the subject selects the optional group(s) to be used, such as depicted, preferably the groups are displayed or otherwise communicated to the subject directly via the device. The options may be portrayed on a visual display, may be communicated to the subject's smartphone, personal computer, or other such device, presented audibly, or by any other similar method for presenting the options to the subject. Preferably, the optional groups are presented to the subject in a manner that is simple and easy for him or her, presumably a layperson, to understand, and not as a list of complex parameters or settings that a layperson would not be qualified to interpret and select for purposes of medical treatment. By way of non-limiting example, the groups can be presented to the subject in terms of the identified or determined activity, movement or motion the subject may be performing, or the identified or determined symptom(s) or side effect(s) he or she may be experiencing, thus allowing the subject to select the group that would best meet his or her needs for the particular activity, symptom or side effect. Other means of presenting the groups for easy layperson subject selection are contemplated as well. For embodiments where the clinician, physician or technician selects at least one optional group, preferably the groups are transmitted to the clinician at a remote location, along with at least one, though preferably all of the identified or determined activity, movement or motion, identified or determined symptom(s) and/or side effect(s), as well as the data corresponding to the measured and quantified motor symptoms and/or side effects. The clinician can then analyze the full spectrum of information and determine which group is the best option for the subject using the identified items, the measured and quantified data, and the proposed groups. The clinician, physician or technician then further has the optional ability to edit any of the optional groups, or create an entirely new group. Alternatively, the system can be completely automated, such as a closed-loop therapy system, whereby the system determines which of the calculated optional groups is optimal in light of the identified or determined factors including activity, movement or motion and symptom(s) and/or side effect(s). In such automated systems, all calculation and selection may be internal and substantially in real-time, with no need to display or transmit options or data. As such, the selected optional group may more rapidly be implemented by the therapy device and more rapidly provide the beneficial therapy to the subject.

Once the optional group(s) of parameters or settings has been calculated by the processor and its algorithm(s) 106 and selected 108, the selected optional group of parameters or settings is then transmitted to the subject's therapy device 110 (e.g., DBS device). The therapy device preferably comprises at least one electronic component for receiving such signals, and uses this at least one component to receive a signal(s) comprising the selected optional group(s) of therapy parameters or settings from the diagnostic device, or from another programming device such as in the possession of the clinician, physician or technician. The parameters or settings may additionally transmitted via wireless communication, as described herein, between the subject's therapy device and a remote location such as to a database or server for storage, or directly to a remote clinician physician or technician for optional review. Once the therapy device receives the transmitted 110 selected optional group(s) of therapy parameters or settings, the selection optional group(s) of parameters or settings is then entered in to the subject's therapy device 112 for the device to provide therapy according to those parameters or settings. As such, the new, selected optional group of parameters or settings is programmed into the subject's therapy device, and the device then operates according to those new parameters or settings 112 and provides that newly determined course of therapy or treatment to the subject.

FIG. 13 depicts a flow chart representing a method embodiment of the present invention for tuning the movement disorder diagnostic device with new therapy parameters or settings. This particular embodiment is envisioned to be performed in an automated fashion, by an algorithm(s), with little or no interaction required with a clinician, physician or technician, and where the subject is located remotely from a clinician, physician or technician, such as at home. First, a subject who has a therapy device, such as a DBS therapy device, wears a movement disorder diagnostic device 90. The therapy device preferably has a first level of therapy parameters or settings already programmed or entered into the device. The movement disorder diagnostic device is as described herein, but preferably comprises at least one physiological or movement sensor for measuring the subject's external body motion, or some other physiological signal of the subject, where the sensor(s) has a signal related to the subject's motion or other physiological signal. Preferably, the movement disorder diagnostic device is a single unit, though may be multiple units (e.g., separate sensor unit and command/transceiver module), and is adapted to be worn or attached to a portion of the subject's body such that the sensor(s) of the movement disorder diagnostic device are able to measure the movement of that particular portion of the subject's body.

Once the subject is wearing the movement disorder diagnostic device 90, the diagnostic device then identifies or determines what, if any, activity, movement or motion the subject is performing 92. This identification or determination is made based on the signal from the at least one physiological or movement sensor of the diagnostic device. The identification or determination may be made by the subject who may manually input the new activity, movement or motion into the device, by a clinician either local or remote to the subject, who may interpret the signals from the at least one sensor and identify or determine the activity, movement or motion, or more preferably, automatically by the device. Automatic identification or determination of the subject's activity, movement, or motion is preferably performed by a processor contained within the device which may include a separate algorithm for activity identification or determination, or the measurement and quantification algorithms may be able to perform this function. Preferably, even where the subject or a clinician, physician or technician identifies or determines and inputs the activity, movement or motion, the device uses the signal from the at least one sensor to estimate or predict the activity, movement or motion, and allows the particular user to confirm the identified or determined activity, movement or motion or to override the suggested identification and input another. The system may provide the user with several options to select from as well, all based on the signals from the at least one sensor.

Once the subject's activity, movement or motion has been identified or determined 92, the next step is to identify or determine any motor symptoms of a movement disorder or side effects of a treatment 94 that the subject is experiencing as experienced while performing the identified or determined movement, motion or activity. The identification or determination of symptoms or side effects 94 is similarly capable of being performed by either a human user or automatically, or combination thereof. Much like the activity identification 92 above, the subject or a clinician may input into the system any known symptoms or side effects. Preferably though, the system automatically identifies or determines the symptoms or side effects based at least in part on the signal from the at least one sensor, and at least in part on the identified or determined activity, movement or motion. Again, a separate symptom and side effect identification algorithm may utilize the signal from the sensor and the identified activity to identify or determine what symptoms and/or side effects the subject is experiencing or has experienced. This algorithm may then present options to the subject or to a clinician, again either local or remote, for confirmation or to be rejected.

Next, the step of measuring and quantifying the identified or determined motor symptoms or side effects 96 of the subject based at least in part on the signal of the at least one sensor(s). The movement disorder diagnostic device uses the signal of the at least one sensor(s) to provide an objective measurement and quantification of the severity of the subject's motor symptoms or side effects while the subject performs the identified or determined activity, movement or motion. The measured and quantified motor symptoms or side effects may include specific movement disorder symptoms, side effects from medication and/or therapy, or combinations thereof. The trained scoring algorithms of the movement disorder diagnostic device perform various measurements and calculations to provide this objective quantification of the subject's motor symptoms.

Once the subject's motor symptoms or side effects have been measured and quantified 96, data corresponding to these measured and quantified motor symptoms is entered into a processor comprising an algorithm(s) 98 for automated analysis. Typically, the data corresponding to the measured and quantified motor symptoms is an objective score, as described herein, but may be represented in numerous ways and means. Preferably, the quantified movement data is entered 98 directly and automatically into the processor and into the determination algorithm(s) without the need for any manual human intervention, such as keying in the data.

The processor and its algorithm(s) then analyze the measured and quantified movement data and calculate a second level of therapy parameters or settings 100. This second level of parameters or settings preferably corresponds to a mode of therapy or treatment that addresses the subject's needs as determined based on the identified or determined activity and symptoms or side effects, and the measured and quantified motor symptom data, as well as other data, goals, or objectives. In other words, if the system determines that, for example, the subject is experiencing a very strong tremor while driving, such determination being made as a result of the identification of the subject's activity and symptoms as well as measurement of the subject's movement and quantifying the severity of the tremor, the processor and algorithm would provide a second level of parameters or settings 100 that would reduce or minimize the tremor the subject is experiencing. As noted, the calculation of a second level of parameters or settings may be based on any number of constraints or desired results for the subject, not solely the immediate symptom or side effect the subject is experiencing. For example, if the subject's main concern is reducing or minimizing symptom occurrence and or severity, the processor and its algorithms will take this desired goal into account when calculating the second level of parameters or settings. Similarly, the calculation of settings or parameters may be based on a desired reduction or minimization of side effects from medication or the therapy. Also, the calculation may be made to balance multiple desired results, such as if a slightly higher rate of occurrence of symptoms is acceptable to the subject in exchange for a minimization of side effects. Other examples of desired results that may be used to determine the second level of therapy parameters or settings for all embodiments include, but are not limited to, a therapeutic window (in terms of time or some other factor) in which the subject most positively responds to therapy, battery life, and other such constraints that might be considered in terms of optimizing the therapy parameters or settings. In any determination, the subject and the clinician, physician or technician decide what the initial desired result is, and these desired results, goals, or constraints are programmed into the processor and its algorithm(s) for the decision making process. In some embodiments, the desired result, goal or constraints may be edited, either by a clinician, physician or technician, or by the subject, in order to allow the algorithm to most accurately analyze the data in light of the optimal treatment for the subject.

Once this second level of parameters or settings has been calculated by the processor and its algorithm(s) 100, the parameters or settings are then transmitted to the subject's therapy device 102 (e.g., DBS device). The therapy device preferably comprises at least one electronic component for receiving such signals, and uses this at least one component to receive a signal(s) comprising the second level of therapy parameters or settings from the diagnostic device. The parameters or settings may additionally transmitted via wireless communication, as described herein, between the subject's therapy device and a remote location such as to a database or server for storage, or directly to a remote clinician physician or technician for optional review. Once the therapy device receives the transmitted 102 second level of therapy parameters or settings, the second level of parameters or settings is then entered in to the subject's therapy device 104 for the device to provide therapy according to those parameters or settings. In the present embodiment, the second level of therapy parameters or settings is an optimized group of settings that is calculated to address each of the identified or determined symptoms and side effects to at least some degree. Optimization preferably takes into account the identified or determined activity, movement or motion as well as the identified or determined symptoms and side effects to calculate a second level of therapy parameters or settings that provides the subject with as much relief from each of the symptoms and side effects as possible, while maximizing the subject's ability to perform the activity, movement or motion with the greatest degree of ability, comfort and safety. As such, the new, second level of parameters or settings is programmed into the subject's therapy device, and the device then operates according to those new parameters or settings 104 and provides that newly determined course of therapy or treatment to the subject.

FIG. 14 is a flow chart depicting one embodiment of a process by which the tuning algorithm(s) is used to determine therapy parameters or settings and program those settings into a subject's therapy device. Like the embodiments described above, data corresponding to measured and quantified motor symptoms is entered into a processor with tuning algorithm(s) 130 for automated analysis. Typically, the data corresponding to the measured and quantified motor symptoms is an objective score, as described herein, but may be represented in numerous ways and means, and may additionally contain information and/or data corresponding to the identified or determined activity, movement or motion and motor symptom(s) and/or side effect(s). Preferably, the quantified movement data is entered directly and automatically into the processor and its determination algorithm(s) 130 without the need for any manual human intervention, such as keying in the data.

The processor and its determination algorithm(s) then analyzes the identified or determined data and the measured and quantified movement data and calculates a second level of therapy parameters or settings 132. This second level of parameters or settings preferably corresponds to a mode of therapy or treatment that addresses the subject's needs as determined based on the measured and quantified motor symptom data, as well as other data, goals, or objectives. In other words, if the system determines that the subject is experiencing a very strong tremor, such determination being made as a result of the identified activity and symptoms/side effects and measurement of the subject's movement and quantifying the severity of the tremor, the processor and algorithm would provide a second level of parameters or settings 132 that would reduce or minimize the tremor the subject is experiencing. As noted, the calculation of a second level of parameters or settings may be based on any number of constraints or desired results for the subject, not solely the identified symptom(s) or side effect(s) the subject is experiencing. Also, the calculation may be made to balance multiple desired results, such as if a slightly higher rate of occurrence of symptoms is acceptable to the subject in exchange for a minimization of side effects. Other examples of desired results that may be used to calculate the second level of therapy parameters or settings for all embodiments include, but are not limited to, a therapeutic window (in terms of time or some other factor) in which the subject most positively responds to therapy, battery life, and other such constraints that might be considered in terms of optimizing the therapy parameters or settings. In any determination, the subject and the clinician, physician or technician decide what the initial desired result is, and these desired results, goals, or constraints are programmed into the algorithm for the decision making process. In some embodiments, the desired result, goal or constraints may be edited, either by a clinician, physician or technician, or by the subject, in order to allow the algorithm to most accurately analyze the data in light of the optimal treatment for the subject.

In the depicted embodiment, the second level of therapy parameters or settings is then transmitted to a clinician at a remote location, and output or displayed for review 134 prior to implementation. Once the second level (or selected group, or selected and combined group) has been transmitted and presented to the clinician, the clinician can analyze the algorithm-generated settings to ensure they are within acceptable safety limits, as well as ensuring that they do in fact address the needs of the subject. Preferably, the transmission of the second settings is performed wirelessly and securely, particularly where the subject is located remotely from the clinician, physician or technician. The parameters or settings are preferably displayed visually to the clinician, physician or technician, such as on a monitor or display device. The parameters or settings may be displayed in any format known to those skilled in the art, including, but not limited to a textual report such as in an email or text document sent to the care provider, transmitted to a software package that displays the parameters or settings in a particular graphical user interface, or transmitted to a centralized or cloud-based database where the clinician, physician or technician can access them for review. Where the parameters or settings are viewed in a software package, the present invention is intended to be adaptable or formattable such that the parameter or settings report or transmission may be used in any commercially available package, or the like.

When the clinician, physician or technician reviews the parameters or settings, he or she then has the option of approving or rejecting the parameters or settings 136. The decision to approve or deny is preferably based on the likelihood that the algorithm(s)′ provided parameters or settings will achieve the subject's desired outcome or goal, and any other constraints that have been elected either by the subject, or by the clinician, physician or technician in conjunction with the subject. If the clinician, physician or technician determines that the parameters or settings will not meet the desired results, goals or constraints, then the settings or parameters can be rejected 138, this rejection is communicated or transmitted back to the subject's diagnostic device, and the determination algorithm(s) repeats the process of using the measured and quantified motor symptom data to provide another set of therapy parameters or settings 132. New sensor recordings, identifications of activity, symptoms and/or side effects may also be made once the parameters or settings have been rejected. This process may be repeated iteratively until acceptable parameters or settings are achieved. Optionally, the clinician, physician or technician may be given the ability to suggest starting parameters for the algorithm to begin with, thus circumventing the need for further motor symptom measurement and quantification. When the algorithm(s) are triggered to provide another iteration of parameters or settings, the above steps are repeated until the parameters or settings meet or achieve the desired end result for therapy.

When the clinician determines that the parameters or settings are, in fact, likely to meet those goals, then he or she approves the parameters or settings 140, and the second level, selected group or combined selected group of parameters or settings is transmitted from the clinician at a remote location back to the subject's therapy device or diagnostic device, and entered automatically and wirelessly into the subject's therapy device 142 in the same or similar manner as described herein. As such, the new, selected group of parameters or settings is programmed into the subject's therapy device, and the device then operates according to those new parameters or settings 144 and provides that newly determined course of therapy or treatment to the subject.

FIG. 15 depicts an operating embodiment of the present invention beginning with the initial occurrence of movement disorder symptoms and ending with continuing monitoring and treatment or therapy. The depicted embodiment describes a system involving continuous home monitoring such that the device operates virtually full-time, monitoring, measuring and analyzing the subject's movement data continually; however, alternative embodiments exist that operate similarly, but with part-time or on-demand monitoring, measuring and analysis. In either embodiment, the subject initially exhibits symptoms of a movement disorder and attends an appointment with a clinician 160. At the appointment, the clinician examines the subject 162, and upon analysis, diagnoses the subject as having a movement disorder, and subsequently orders a course of treatment or therapy 164. At a follow-up visit, the subject may report that symptoms fluctuate greatly throughout the day 166, even while on the prescribed treatment or therapy plan. The clinician then decides to perform in-home continual testing 168 to better determine the severity of the subject's symptoms. Continuous home monitoring 170 begins with the initial programming 172 of any movement monitoring devices or automated treatment delivery devices for the subject, or instructing the subject how to do so. This initial programming may be performed in-clinic or remotely, and may be performed manually or integrated with the review of a clinician, physician or technician, or may be performed intelligently in an automated or semi-automated process utilizing trained tuning algorithms. The device, containing at least one sensor, preferably an accelerometer and/or gyroscope of at least three axes, but optionally another sensor capable of measuring motion, such as an EMG, continually records the subject's movement during activities of daily living 174. In addition, the device can include two or more types of sensors, preferably accelerometers and gyroscopes. Activities of daily living may include folding laundry, handwriting, eating, dressing, self-care, walking, running, and the like. Optionally, the clinician may order the subject to perform clinical tasks such as finger tapping, nose touching, or the like, as defined by standardized scales such as the UPDRS, TRS, and the like, at regularly scheduled periods. Such movement data would also be continually recorded.

A trained tuning algorithm, preferably incorporated by at least one computer processor, analyzes the recorded movement data 176. This data analysis may be performed at a scheduled time where the data is uploaded to the algorithm and analyzed, or, more preferably, may be performed in real time. The algorithm and processor function to distinguish voluntary motion of activities of daily living or clinician ordered tasks from movement disorder symptoms and quantify their severity. Preferably the trained algorithms and computer processor are also in two-way communication 178 with a central database 180 or multiple databases made up of previous patient movement data, disorder histories, treatment histories, and the like. Preferably two or more databases are used for reading. Such a database 180 would preferably retain information from the current subject for use with future subjects 182 (maintaining subject privacy and confidentiality at all times) and work with the trained algorithms and processor to determine a recommended treatment for the current subject based on the previous patient data. This database 180 could optionally be used as a real-time gateway for providing updates to the subject's clinician 184 regarding the subject's status.

If the trained algorithms and processor determine 176 that the patient still suffers from movement disorder symptoms 186, a new course of treatment or therapy is determined 188 either based solely on the subject's measured and quantified symptoms, in conjunction with the central database 180 as previously described, or a combination of the two. If the subject has an automated treatment delivery device, it is preferably reprogrammed according to the new treatment protocol 190. Preferably, this reprogramming of the subject's device is performed remotely, and in an automated, intelligent fashion requiring little, or more preferably no interaction from a clinician, physician or technician. The movement measuring device then continues to record new movement data and the process repeats. If the trained algorithms and processor determine 176 movement disorder symptoms no longer persist 192 then no new treatment is needed, no changes are made 194, and the device continues recording movement data.

FIG. 16 is a flow chart depicting one example of a tuning algorithm used to determine the therapy parameters or settings that can be programmed into a subject's therapy device for another iteration of testing or for providing therapy according to those parameters or settings. This particularly depicted embodiment is merely an example, and many other combinations of parameters or settings, number of variables and/or steps are envisioned. First, a set of initial parameters 200 is programmed into the subject's therapy device. These initial parameters may be pre-programmed, programmed manually or as part of an integrated system, or automatically programmed via an intelligent algorithm. Further, the initial parameters may define any number of variables or parameters or settings as disclosed herein to be set while others are altered or modified through the tuning process. In the depicted embodiment, some initial parameters or settings include a pulse width (PW) of 90 μs, frequency (F) of 130 Hz, and pulse amplitude (PA) of 0 mA. An amplitude of 0 mA essentially means that no therapeutic current is being provided, or the therapy is turned off, but is provided as an initial setting to being the testing process from which the amplitude is increased gradually throughout the testing iterations. Once these parameters are programmed into the subject's therapy device such that the device can provide therapy according to these initial parameters or settings, the tuning process may begin by initially increasing the pulse amplitude 202 to a first test level provided to the subject, in the figure shown to be an increase of 0.25 mA. As the therapy is provided to the subject according to the initial parameters and the first level of test parameters, the movement disorder diagnostic device (not shown) measures and quantifies the subject's movement (not shown) in order to determine the level of symptoms, side effects, and the like that the subject experiences while receiving therapy according to the provided parameters or settings. Based on the measured and quantified movement data, the depicted algorithm first determines whether side effects are occurring 204. If side effects occur under the provided therapy parameters or settings, then the algorithm may determine that using the presently tested contact may result in persistent side effects, and move on to the next contact 218 to test and determine whether therapy can be provided in an alternative pattern from the different contacts to avoid the side effects.

If side effects do not occur or do not persist from one iteration of the test to the next, then the algorithm then determines whether the subject's symptoms have improved 206 as a result of the tested therapy parameters or settings. This determination, too, is based on the measured and quantified movement data provided by the movement disorder diagnostic device. If the algorithm determines that the symptoms have not improved based on the provided therapy parameters or settings, it may next perform a query to see how many individual parameters or settings have been tested (i.e., changed individually to determine the particular parameter's effect on the subject's side effects and/or symptoms), or how many groups of parameters or settings have been tested 214. In the depicted embodiment, the threshold value is eight parameters/settings or groups thereof. Therefore, in the depicted embodiment, if eight parameters/settings or groups thereof have been tested through the depicted process, the algorithm determines whether there has been any improvement in the last four changes in parameters/settings or groups 216. If there has been no such improvement, then again, the algorithm may move on to the next contact 218 in order to try and achieve a therapeutic response from another contact that may address the subject's symptoms and/or side effects and other constraints.

If, however, the depicted algorithm determines that a) the subject's symptoms did improve under the provided therapy parameters or settings 206; b) symptoms did not improve and fewer than eight parameters/settings or groups thereof have been tested 214; or c) at least eight parameters/settings or group have been tested, but there has been some improvement within the last four iterations 216, then the algorithm next determines whether the therapy parameters or settings are being provided at a maximum value for one of those parameters or settings 208—in the depicted case pulse amplitude. Pre-determined parameter or settings limits may be defined to keep the therapy device operating within safe limits in order to protect the subject. If the specific parameter or setting that is being tested (in the depicted case, pulse amplitude) has not reached its maximum value, then the algorithm may again increase the value of that parameter or setting 202 and repeat the process. If the maximum value has already been reached for the particular parameter or setting, then that variable cannot be increased any further, and the algorithm determines whether another individual parameter or setting is at its maximum level 210—in the depicted case frequency. If the frequency has not reached its maximum value yet, then the algorithm reduces the amplitude to zero, increases the frequency 220 and then again begins the iterative process by initially increasing the amplitude 202 to provide therapy to the subject at the new levels of parameters or settings.

If, however, symptoms have shown improvement as a result in the change in therapy parameters or settings, and both of the first two parameters or settings have reached their maximum levels, then the algorithm next determines if yet another parameter or setting (in the depicted case, pulse width) has yet reached its maximum value 212. If all of the parameters or settings desired to be tested have reached their maximums, then the algorithm determines that the tested contact, though close, may not provide the best course of therapy, and moves on to the next contact 218 to begin the process over. If, however, the next parameter or setting 212 has not reached its maximum value, the algorithm again reduces the amplitude to zero, maintains the maximum frequency, and increases the pulse width 222 (again, these are exemplary parameters or settings, and other combinations are envisioned). Once the updated parameters or settings have been determined, the algorithm again begins the process over by slightly increasing the amplitude 202 to provide a therapeutic current according to the new settings, and repeats the testing process. This decision process is repeated by the intelligent algorithm until it determines that the best combination of contact(s) and parameters or settings has been achieved, resulting in an optimized therapy that takes into account the subject's side effects, symptoms, and/or other constraints.

FIG. 17 depicts a flow chart representing a method embodiment of the present invention for tuning the movement disorder diagnostic device with new therapy parameters or settings. First, a movement disorder diagnostic device is provided to a subject 270 who has a therapy device, such as a DBS therapy device. The movement disorder diagnostic device is as described herein, but preferably comprises at least one physiological or movement sensor for measuring the subject's external body motion, or some other physiological signal of the subject, where the sensor(s) has a signal related to the subject's motion or other physiological signal. Preferably, the movement disorder diagnostic device is a single unit, though may be multiple units (e.g., separate sensor unit and command/transceiver module), and is adapted to be worn or attached to a portion of the subject's body such that the sensor(s) of the movement disorder diagnostic device are able to measure the movement of that particular portion of the subject's body.

Once the movement disorder diagnostic device has been provided 270 to the subject, and the subject has donned or attached the device, the next step is to instruct the subject 272 to perform at least one movement disorder test(s) while the subject is undergoing therapy from the therapy (e.g., DBS) device, or is under the effects of recently administered therapy therefrom. These instructions may include a list of which test(s) the subject is to perform, directions on how to perform the test, or a combination of both. The instructions to perform a test or tests may be given 272 in person (if in a clinical setting) by a clinician, physician or technician, or more preferably may be provided in an automated or electronic fashion. For example, the instruction to perform the test(s) may be provided in an automated fashion to the subject via a video display, or a notification or alert message provided via such a display or perhaps the subject's smart phone, on a display device via tele- or videoconference with a clinician, physician or technician, or the like. The instructions are provided 272, either audibly, visually, or a combination thereof. The subject's performance of the tasks is thus affected by the therapy being provided, or recently provided by the therapy device.

While the subject is performing the at least one movement disorder test(s) as instructed 272, the step of measuring and quantifying motor symptoms 274 of the subject based at least in part on the signal of the at least one sensor(s) is performed. The movement disorder diagnostic device uses the signal of the at least one sensor(s) to provide an objective measurement and quantification of the severity of the subject's motor symptoms while the subject performs the at least one movement disorder test. The measured and quantified motor symptoms may include specific movement disorder symptoms, side effects from medication and/or therapy, or combinations thereof. The trained scoring algorithms of the movement disorder diagnostic device perform various measurements and calculations to provide this objective quantification of the subject's motor symptoms.

Once the subject's motor symptoms have been measured and quantified 274, data corresponding to these measured and quantified motor symptoms is transmitted to a remote location 276. Typically, the data corresponding to the measured and quantified motor symptoms is an objective score, as described herein, but may be represented in numerous ways and means. Preferably, the data is transmitted 276 directly from the subject's movement disorder diagnostic device using at least on electrical component for sending or transmitting signals. Preferably, such transmission is performed wirelessly as described herein, though it may be performed by use of a tethered or docking station system where the diagnostic device is placed in contact with the tethered communications system or docking station which then transmits the data. As described herein, the data is preferably transmitted directly to a remote location where a clinician, physician or technician can access the data immediately or at will for analysis and review. Additionally or alternatively, the data may be transmitted to a form of data storage such as a centralized server, cloud-based server, or other storage database. The clinician, physician or technician can then either access the data directly from a computer device to which the data is transmitted 276 or from the storage system in order to review and analyze the data. Using this data, the clinician, physician or technician then determines a second level of therapy (e.g., DBS) parameters or settings 278 based on the data and with the goal of improving the subject's movement in a manner as described herein. This second level of parameters or settings preferably corresponds to a mode of therapy or treatment that addresses the subject's needs as determined based on the measured and quantified motor symptom data, as well as other data, goals, or objectives. In other words, if the clinician or system determines that the subject is experiencing a very strong tremor, such determination being made as a result of the measurement of the subject's movement and quantifying the severity of the tremor, the second level of parameters or settings 278 would aim to reduce or minimize the tremor the subject is experiencing. As noted, the determination of a second level of parameters or settings may be based on any number of constraints or desired results for the subject, not solely the immediate symptom or side effect the subject is experiencing. For example, if the subject's main concern is reducing or minimizing symptom occurrence and or severity, the clinician, physician or technician, or algorithm will take this desired goal into account when determining the second level of parameters or settings. Similarly, the determination of settings or parameters may be based on a desired reduction or minimization of side effects from medication or the therapy. Also, the determination may be made to balance multiple desired results, such as if a slightly higher rate of occurrence of symptoms is acceptable to the subject in exchange for a minimization of side effects. Other examples of desired results that may be used to determine the second level of therapy parameters or settings for all embodiments include, but are not limited to, a therapeutic window (in terms of time or some other factor) in which the subject most positively responds to therapy, battery life, and other such constraints that might be considered in terms of optimizing the therapy parameters or settings. In any determination, the subject and the clinician, physician or technician decide what the initial desired result is, and these desired results, goals, or constraints are programmed into the algorithm for the decision making process. In some embodiments, the desired result, goal or constraints may be edited or changed in order to allow the second level of parameters to provide optimal treatment for the subject.

Once the second level of therapy parameters or settings has been determined or decided upon, this second level of parameters or settings is then transmitted to the subject's therapy device 279 (e.g., DBS device). The therapy device preferably comprises at least one electronic component for receiving such signals, and uses this at least one component to receive a signal(s) comprising the second level of therapy parameters or settings. The parameters or settings are preferably transmitted 279 via wireless communication, as described herein, between the remote location and the subject's therapy device. Once the therapy device receives the transmitted second level of therapy parameters or settings, the second level of parameters or settings is then entered in to the subject's therapy device 280 for the device to provide therapy according to those parameters or settings 282. As such, the new, second level of parameters or settings is programmed into the subject's therapy device, and the device then operates according to those new parameters or settings 282 and provides that newly determined course of therapy or treatment to the subject.

FIG. 18 depicts a flow chart representing a method embodiment of the present invention for tuning the movement disorder diagnostic device with new therapy parameters or settings. First, a movement disorder diagnostic device is provided to a subject 370 who has a therapy device, such as a DBS therapy device. The movement disorder diagnostic device is as described herein, but preferably comprises at least one physiological or movement sensor for measuring the subject's external body motion, or some other physiological signal of the subject, where the sensor(s) has a signal related to the subject's motion or other physiological signal. Preferably, the movement disorder diagnostic device is a single unit, though may be multiple units (e.g., separate sensor unit and command/transceiver module), and is adapted to be worn or attached to a portion of the subject's body such that the sensor(s) of the movement disorder diagnostic device are able to measure the movement of that particular portion of the subject's body.

Once the movement disorder diagnostic device has been provided 370 to the subject, and the subject has donned or attached the device, the next step is to instruct the subject 372 to perform at least one movement disorder test(s) while the subject is undergoing therapy from the therapy (e.g., DBS) device, or is under the effects of recently administered therapy therefrom. These instructions may include a list of which test(s) the subject is to perform, directions on how to perform the test, or a combination of both. The instructions to perform a test or tests may be given 372 in person (if in a clinical setting) by a clinician, physician or technician, or more preferably may be provided in an automated or electronic fashion, such as tele- or videoconference with a clinician, physician or technician. For example, the instruction to perform the test(s) may be provided in an automated fashion to the subject via a video display, or a notification or alert message provided via such a display or perhaps the subject's smart phone, or the like. The instructions are provided 372, either audibly, visually, or a combination thereof. The subject's performance of the tasks is thus affected by the therapy being provided, or recently provided by the therapy device.

While the subject is performing the at least one movement disorder test(s) as instructed 372, the step of measuring and quantifying motor symptoms 374 of the subject based at least in part on the signal of the at least one sensor(s) is performed. The movement disorder diagnostic device uses the signal of the at least one sensor(s) to provide an objective measurement and quantification of the severity of the subject's motor symptoms while the subject performs the at least one movement disorder test. The measured and quantified motor symptoms may include specific movement disorder symptoms, side effects from medication and/or therapy, or combinations thereof. The trained scoring algorithms of the movement disorder diagnostic device perform various measurements and calculations to provide this objective quantification of the subject's motor symptoms.

Once the subject's motor symptoms have been measured and quantified 374, data corresponding to these measured and quantified motor symptoms is transmitted to a remote location 376. Typically, the data corresponding to the measured and quantified motor symptoms is an objective score, as described herein, but may be represented in numerous ways and means. Preferably, the data is transmitted 376 directly from the subject's movement disorder diagnostic device using at least on electrical component for sending or transmitting signals. Preferably, such transmission is performed wirelessly as described herein, though it may be performed by use of a tethered or docking station system where the diagnostic device is placed in contact with the tethered communications system or docking station which then transmits the data. As described herein, the data is preferably transmitted directly to a remote location where a clinician, physician or technician can access the data immediately or at will for analysis and review. Additionally or alternatively, the data may be transmitted to a form of data storage such as a centralized server, cloud-based server, or other storage database. The clinician, physician or technician can then either access the data directly from a computer device to which the data is transmitted 376 or from the storage system in order to review and analyze the data. Using this data, the clinician, physician or technician then determines at least two optional groups of therapy (e.g., DBS) parameters or settings 373 based on the data and with the goal of improving the subject's movement in a manner as described herein. The at least two optional groups of parameters or settings preferably each correspond to a different mode of therapy or treatment that addresses the subject's needs as determined based on the measured and quantified motor symptom data, as well as other data, goals, or objectives. In other words, if the system determines that the subject is experiencing a very strong tremor and gait disturbances, such determination being made as a result of the measurement of the subject's movement and quantifying the severity of the tremor and gait disturbance, one of the at least two optional groups of parameters or settings 378 would aim to reduce or minimize the tremor and the other would aim to reduce or minimize the gait disturbances the subject is experiencing. As noted, the determination of at least two optional groups of parameters or settings may be based on any number of constraints or desired results for the subject, not solely the immediate symptom or side effect the subject is experiencing. For example, if the subject's main concern is reducing or minimizing symptom occurrence and or severity, the clinician, physician or technician, or algorithm will take this desired goal into account when determining the at least two optional groups of parameters or settings. Similarly, the determination of settings or parameters may be based on a desired reduction or minimization of side effects from medication or the therapy. Also, the determination may be made to balance multiple desired results, such as if a slightly higher rate of occurrence of symptoms is acceptable to the subject in exchange for a minimization of side effects. Other examples of desired results that may be used to determine the second level of therapy parameters or settings for all embodiments include, but are not limited to, a therapeutic window (in terms of time or some other factor) in which the subject most positively responds to therapy, battery life, and other such constraints that might be considered in terms of optimizing the therapy parameters or settings. In any determination, the subject and the clinician, physician or technician decide what the initial desired result is, and these desired results, goals, or constraints are programmed into the algorithm for the decision making process. In some embodiments, the desired result, goal or constraints may be edited or changed in order to allow the second level of parameters to provide optimal treatment for the subject.

Once the at least two optional groups of therapy parameters or settings has been determined or decided upon, at least one of the optional groups is selected for use 377. Then, the at least one selected optional group of parameters or settings is then transmitted to the subject's therapy device 388 (e.g., DBS device). The therapy device preferably comprises at least one electronic component for receiving such signals, and uses this at least one component to receive a signal(s) comprising the second level of therapy parameters or settings. The at least one selected optional group of parameters or settings are preferably transmitted 388 via wireless communication, as described herein, between the remote location and the subject's therapy device. Once the therapy device receives the transmitted second level of therapy parameters or settings, the selected optional group of parameters or settings is then entered in to the subject's therapy device 390 for the device to provide therapy according to those parameters or settings 392. As such, the new, selected optional group of parameters or settings is programmed into the subject's therapy device, and the device then operates according to those new parameters or settings 392 and provides that newly determined course of therapy or treatment to the subject.

FIG. 19 depicts a flow chart representing a method embodiment of the present invention for tuning the movement disorder diagnostic device with new therapy parameters or settings. First, a movement disorder diagnostic device is provided to a subject 470 who has a therapy device, such as a DBS therapy device. The movement disorder diagnostic device is as described herein, but preferably comprises at least one physiological or movement sensor for measuring the subject's external body motion, or some other physiological signal of the subject, where the sensor(s) has a signal related to the subject's motion or other physiological signal. Preferably, the movement disorder diagnostic device is a single unit, though may be multiple units (e.g., separate sensor unit and command/transceiver module), and is adapted to be worn or attached to a portion of the subject's body such that the sensor(s) of the movement disorder diagnostic device are able to measure the movement of that particular portion of the subject's body.

Once the movement disorder diagnostic device has been provided 470 to the subject, and the subject has donned or attached the device, the next step is to instruct the subject 472 to perform at least one movement disorder test(s) while the subject is undergoing therapy from the therapy (e.g., DBS) device, or is under the effects of recently administered therapy therefrom. These instructions may include a list of which test(s) the subject is to perform, directions on how to perform the test, or a combination of both. The instructions to perform a test or tests may be given 472 in person (if in a clinical setting) by a clinician, physician or technician, or more preferably may be provided in an automated or electronic fashion. For example, the instruction to perform the test(s) may be provided in an automated fashion to the subject via a video display, or a notification or alert message provided via such a display or perhaps the subject's smart phone, on a display device via tele- or videoconference with a clinician, physician or technician, or the like. The instructions are provided 472, either audibly, visually, or a combination thereof. The subject's performance of the tasks is thus affected by the therapy being provided, or recently provided by the therapy device.

While the subject is performing the at least one movement disorder test(s) as instructed 472, the step of measuring and quantifying motor symptoms 474 of the subject based at least in part on the signal of the at least one sensor(s) is performed. The movement disorder diagnostic device uses the signal of the at least one sensor(s) to provide an objective measurement and quantification of the severity of the subject's motor symptoms while the subject performs the at least one movement disorder test. The measured and quantified motor symptoms may include specific movement disorder symptoms, side effects from medication and/or therapy, or combinations thereof. The trained scoring algorithms of the movement disorder diagnostic device perform various measurements and calculations to provide this objective quantification of the subject's motor symptoms.

Once the subject's motor symptoms have been measured and quantified 474, data corresponding to these measured and quantified motor symptoms is transmitted to a remote location 476. Typically, the data corresponding to the measured and quantified motor symptoms is an objective score, as described herein, but may be represented in numerous ways and means. Preferably, the data is transmitted 476 directly from the subject's movement disorder diagnostic device using at least on electrical component for sending or transmitting signals. Preferably, such transmission is performed wirelessly as described herein, though it may be performed by use of a tethered or docking station system where the diagnostic device is placed in contact with the tethered communications system or docking station which then transmits the data. As described herein, the data is preferably transmitted directly to a remote location where a clinician, physician or technician can access the data immediately or at will for analysis and review. Additionally or alternatively, the data may be transmitted to a form of data storage such as a centralized server, cloud-based server, or other storage database. The clinician, physician or technician can then either access the data directly from a computer device to which the data is transmitted 476 or from the storage system in order to review and analyze the data. Using this data, the clinician, physician or technician then determines a second level of therapy (e.g., DBS) parameters or settings 478 based on the data and with the goal of improving the subject's movement in a manner as described herein. This second level of parameters or settings preferably corresponds to a mode of therapy or treatment that addresses the subject's needs as determined based on the measured and quantified motor symptom data, as well as other data, goals, or objectives. In other words, if the clinician or system determines that the subject is experiencing a very strong tremor, such determination being made as a result of the measurement of the subject's movement and quantifying the severity of the tremor, the second level of parameters or settings 478 would aim to reduce or minimize the tremor the subject is experiencing. As noted, the determination of a second level of parameters or settings may be based on any number of constraints or desired results for the subject, not solely the immediate symptom or side effect the subject is experiencing. For example, if the subject's main concern is reducing or minimizing symptom occurrence and or severity, the clinician, physician or technician, or algorithm will take this desired goal into account when determining the second level of parameters or settings. Similarly, the determination of settings or parameters may be based on a desired reduction or minimization of side effects from medication or the therapy. Also, the determination may be made to balance multiple desired results, such as if a slightly higher rate of occurrence of symptoms is acceptable to the subject in exchange for a minimization of side effects. Other examples of desired results that may be used to determine the second level of therapy parameters or settings for all embodiments include, but are not limited to, a therapeutic window (in terms of time or some other factor) in which the subject most positively responds to therapy, battery life, and other such constraints that might be considered in terms of optimizing the therapy parameters or settings. In any determination, the subject and the clinician, physician or technician decide what the initial desired result is, and these desired results, goals, or constraints are programmed into the algorithm for the decision making process. In some embodiments, the desired result, goal or constraints may be edited or changed in order to allow the second level of parameters to provide optimal treatment for the subject.

Once the second level of therapy parameters or settings has been determined or decided upon, this second level of parameters or settings is then transmitted to the subject's therapy device 479 (e.g., DBS device). The therapy device preferably comprises at least one electronic component for receiving such signals, and uses this at least one component to receive a signal(s) comprising the second level of therapy parameters or settings. The parameters or settings are preferably transmitted 479 via wireless communication, as described herein, between the remote location and the subject's therapy device. Preferably, the clinician, physician or technician updates the settings or parameters in the software package and these parameters or settings are transmitted and automatically entered 481 into the subject's device requiring no direct interaction between the clinician, physician or technician and the subject's therapy device. Thus, once the therapy device receives the transmitted second level of therapy parameters or settings, the second level of parameters or settings is then automatically entered in to the subject's therapy device 481 for the device to provide therapy according to those parameters or settings 482. As such, the new, second level of parameters or settings is programmed into the subject's therapy device, and the device then operates according to those new parameters or settings 482 and provides that newly determined course of therapy or treatment to the subject. Thus, the system measures and quantifies the subject's symptoms 474, allows a remotely located clinician to determine a second level of therapy parameters or settings that would address those symptoms 478, the second level of parameters or settings is transmitted 479 and automatically programmed 481 into the subject's therapy device 480, and the therapy device implements the second level of parameters or setting to in fact address the subject's symptoms 482.

FIG. 20 depicts a flow chart representing a method embodiment of the present invention for tuning the movement disorder diagnostic device with new therapy parameters or settings. First, a movement disorder diagnostic device is provided to a subject 570 who has a therapy device, such as a DBS therapy device. The movement disorder diagnostic device is as described herein, but preferably comprises at least one physiological or movement sensor for measuring the subject's external body motion, or some other physiological signal of the subject, where the sensor(s) has a signal related to the subject's motion or other physiological signal. Preferably, the movement disorder diagnostic device is a single unit, though may be multiple units (e.g., separate sensor unit and command/transceiver module), and is adapted to be worn or attached to a portion of the subject's body such that the sensor(s) of the movement disorder diagnostic to device are able to measure the movement of that particular portion of the subject's body.

Once the movement disorder diagnostic device has been provided 570 to the subject, and the subject has donned or attached the device, the next step is to instruct the subject 572 to perform at least one movement disorder test(s) while the subject is undergoing therapy from the therapy (e.g., DBS) device, or is under the effects of recently administered therapy therefrom. These instructions may include a list of which test(s) the subject is to perform, directions on how to perform the test, or a combination of both. The instructions to perform a test or tests may be given 572 in person (if in a clinical setting) by a clinician, physician or technician, or more preferably may be provided in an automated or electronic fashion. For example, the instruction to perform the test(s) may be provided in an automated fashion to the subject via a video display, or a notification or alert message provided via such a display or perhaps the subject's smart phone, on a display device via tele- or videoconference with a clinician, physician or technician, or the like. The instructions are provided 572, either audibly, visually, or a combination thereof. The subject's performance of the tasks is thus affected by the therapy being provided, or recently provided by the therapy device.

While the subject is performing the at least one movement disorder test(s) as instructed 572, the step of measuring and quantifying motor symptoms 574 of the subject based at least in part on the signal of the at least one sensor(s) is performed. The movement disorder diagnostic device uses the signal of the at least one sensor(s) to provide an objective measurement and quantification of the severity of the subject's motor symptoms while the subject performs the at least one movement disorder test. The measured and quantified motor symptoms may include specific movement disorder symptoms, side effects from medication and/or therapy, or combinations thereof. The trained scoring algorithms of the movement disorder diagnostic device perform various measurements and calculations to provide this objective quantification of the subject's motor symptoms.

Once the subject's motor symptoms have been measured and quantified 574, data corresponding to these measured and quantified motor symptoms is transmitted to a remote location 576. Typically, the data corresponding to the measured and quantified motor symptoms is an objective score, as described herein, but may be represented in numerous ways and means. Preferably, the data is transmitted 576 directly from the subject's movement disorder diagnostic device using at least on electrical component for sending or transmitting signals. Preferably, such transmission is performed wirelessly as described herein, though it may be performed by use of a tethered or docking station system where the diagnostic device is placed in contact with the tethered communications system or docking station which then transmits the data. As described herein, the data is preferably transmitted directly to a remote location where a clinician, physician or technician can access the data immediately or at will for analysis and review. Additionally or alternatively, the data may be transmitted to a form of data storage such as a centralized server, cloud-based server, or other storage database. The clinician, physician or technician can then either access the data directly from a computer device to which the data is transmitted 576 or from the storage system in order to review and analyze the data. Using this data, the clinician, physician or technician then determines a second level of therapy (e.g., DBS) parameters or settings 578 based on the data and with the goal of improving the subject's movement in a manner as described herein. This second level of parameters or settings preferably corresponds to a mode of therapy or treatment that addresses the subject's needs as determined based on the measured and quantified motor symptom data, as well as other data, goals, or objectives. In other words, if the clinician or system determines that the subject is experiencing a very strong tremor, such determination being made as a result of the measurement of the subject's movement and quantifying the severity of the tremor, the second level of parameters or settings 578 would aim to reduce or minimize the tremor the subject is experiencing. As noted, the determination of a second level of parameters or settings may be based on any number of constraints or desired results for the subject, not solely the immediate symptom or side effect the subject is experiencing. For example, if the subject's main concern is reducing or minimizing symptom occurrence and or severity, the clinician, physician or technician, or algorithm will take this desired goal into account when determining the second level of parameters or settings. Similarly, the determination of settings or parameters may be based on a desired reduction or minimization of side effects from medication or the therapy. Also, the determination may be made to balance multiple desired results, such as if a slightly higher rate of occurrence of symptoms is acceptable to the subject in exchange for a minimization of side effects. Other examples of desired results that may be used to determine the second level of therapy parameters or settings for all embodiments include, but are not limited to, a therapeutic window (in terms of time or some other factor) in which the subject most positively responds to therapy, battery life, and other such constraints that might be considered in terms of optimizing the therapy parameters or settings. In any determination, the subject and the clinician, physician or technician decide what the initial desired result is, and these desired results, goals, or constraints are programmed into the algorithm for the decision making process. In some embodiments, the desired result, goal or constraints may be edited or changed in order to allow the second level of parameters to provide optimal treatment for the subject.

Once the second level of therapy parameters or settings has been determined or decided upon, this second level of parameters or settings is then transmitted to the subject's therapy device 579 (e.g., DBS device). The therapy device preferably comprises at least one electronic component for receiving such signals, and uses this at least one component to receive a signal(s) comprising the second level of therapy parameters or settings. The parameters or settings are preferably transmitted 579 via wireless communication, as described herein, between the remote location and the subject's therapy device. Once the therapy device receives the transmitted second level of therapy parameters or settings, the second level of parameters or settings is then entered in to the subject's therapy device 580 for the device to provide therapy according to those parameters or settings 582. As such, the new, second level of parameters or settings is programmed into the subject's therapy device, and the device then operates according to those new parameters or settings 582 and provides that newly determined course of therapy or treatment to the subject. In this particular embodiment, the system additionally uploads or transmits 594 the second level of DBS parameters or setting and/or the quantified motor symptom data to a remote location such as a database or server for storage and or access by the clinician at a later time. The uploading or transmission of the parameters or settings and/or data is preferably done substantially simultaneously with transmitting the parameters or settings to the subject's therapy device, however it can be done at a later time. Thus, the system measures and quantifies the subject's symptoms 574, allows a remotely located clinician to determine a second level of therapy parameters or settings that would address those symptoms 578, the second level of parameters or settings is transmitted 579 and programmed into the subject's therapy device 580, and the therapy device implements the second level of parameters or setting to in fact address the subject's symptoms 582, and additionally the parameters or settings and/or the quantified movement data is transmitted to a database or other storage medium 594 for later access and/or storage of the data.

FIG. 21 depicts a normal human brain labeled with various lobes and cortexes known in the art. Of particular note within the scope of the present invention is the motor cortex. The primary motor cortex, or M1, is one of the principal brain areas involved in motor function. M1 is located in the frontal lobe of the brain, along a bump called the precentral gyms. The role of the primary motor cortex is to generate neural impulses that control the execution of movement. Signals from M1 cross the body's midline to activate skeletal muscles on the opposite side of the body, meaning that the left hemisphere of the brain controls the right side of the body, and the right hemisphere controls the left side of the body. Every part of the body is represented in the primary motor cortex, and these representations are arranged somatotopically—the foot is next to the leg which is next to the trunk which is next to the arm and the hand. The amount of brain matter devoted to any particular body part represents the amount of control that the primary motor cortex has over that body part. For example, a lot of cortical space is required to control the complex movements of the hand and fingers, and these body parts have larger representations in M1 than the trunk or legs, whose muscle patterns are relatively simple. This disproportionate map of the body in the motor cortex is called the motor homunculus. Other regions of the cortex involved in motor function are called the secondary motor cortices. These regions include the posterior parietal cortex, the premotor cortex, and the supplementary motor area (SMA). The posterior parietal cortex is involved in transforming visual information into motor commands. For example, the posterior parietal cortex would be involved in determining how to steer the arm to a glass of water based on where the glass is located in space. The posterior parietal areas send this information on to the premotor cortex and the supplementary motor area. The premotor cortex lies just in front of (anterior to) the primary motor cortex. It is involved in the sensory guidance of movement, and controls the more proximal muscles and trunk muscles of the body. In our example, the premotor cortex would help to orient the body before reaching for the glass of water. The supplementary motor area lies above, or medial to, the premotor area, also in front of the primary motor cortex. It is involved in the planning of complex movements and in coordinating two-handed movements. The supplementary motor area and the premotor regions both send information to the primary motor cortex as well as to brainstem motor regions. Neurons in M1, SMA and premotor cortex give rise to the fibers of the corticospinal tract. The corticospinal tract is the only direct pathway from the cortex to the spine and is composed of over a million fibers. These fibers descend through the brainstem where the majority of them cross over to the opposite side of the body. After crossing, the fibers continue to descend through the spine, terminating at the appropriate spinal levels. The corticospinal tract is the main pathway for control of voluntary movement in humans. There are other motor pathways which originate from subcortical groups of motor neurons (nuclei). These pathways control posture and balance, coarse movements of the proximal muscles, and coordinate head, neck and eye movements in response to visual targets. Subcortical pathways can modify voluntary movement through interneuronal circuits in the spine and through projections to cortical motor regions.

FIG. 22 is an image depicting the motor cortex of a human brain with corresponding parts of the body that are controlled by the regions of the motor cortex. The size of the body part in the image is directly related to the amount of the motor cortex that is dedicated to control of that body part. For example the various portions of a person's hand and face are much larger than any portion of the leg. Thus, much more of the motor cortex is directed to control of the hands and face than the leg. Knowledge of these relationships, and mapping of the specific regions of the brain for particular patient which control the body movement, allows the present invention to provide highly-specific, targeted and tuned therapy to the subject to alleviate his or her symptoms.

FIG. 23 depicts a flow chart representing a method embodiment of the present invention for tuning the movement disorder diagnostic device with new therapy parameters or settings. This particular embodiment is envisioned to be performed in an automated fashion, by an algorithm(s), with little or no interaction required with a clinician, physician or technician, and where the subject is located remotely from a clinician, physician or technician, such as at home. First, a subject who has a therapy device, such as a DBS therapy device, wears a movement disorder diagnostic device 90. The DBS device further comprises at least one dual-mode lead comprising a plurality of contacts, each contact adapted to both provide stimulation to the subject's brain as well as to sense or acquire signals from the subject's brain, particularly to measure brain activity. The therapy device preferably has a first level of therapy parameters or settings already programmed or entered into the device. The movement disorder diagnostic device is as described herein, but preferably comprises at least one physiological or movement sensor for measuring the subject's external body motion, or some other physiological signal of the subject, where the sensor(s) has a signal related to the subject's motion or other physiological signal. Preferably, the movement disorder diagnostic device is a single unit, though may be multiple units (e.g., separate sensor unit and command/transceiver module), and is adapted to be worn or attached to a portion of the subject's body such that the sensor(s) of the movement disorder diagnostic device are able to measure the movement of that particular portion of the subject's body.

Once the subject is wearing the movement disorder diagnostic device 600, the diagnostic device then identifies or determines what, if any, activity, movement or motion the subject is performing and brain activity is measured and acquired 602. This identification or determination is made based on the signal from the at least one physiological or movement sensor of the diagnostic device, and the brain activity is measured with a dual-mode DBS lead adapted to provide stimulation as well as measure signals from the subject. The identification or determination may be made by the subject who may manually input the new activity, movement or motion into the device, by a clinician either local or remote to the subject, who may interpret the signals from the at least one sensor and identify or determine the activity, movement or motion, or more preferably, automatically by the device. Automatic identification or determination of the subject's activity, movement, or motion is preferably performed by a processor contained within the device which may include a separate algorithm for activity identification or determination, or the measurement and quantification algorithms may be able to perform this function. Preferably, even where the subject or a clinician, physician or technician identifies or determines and inputs the activity, movement or motion, the device uses the signal from the at least one sensor to estimate or predict the activity, movement or motion, and allows the particular user to confirm the identified or determined activity, movement or motion or to override the suggested identification and input another. The system may provide the user with several options to select from as well, all based on the signals from the at least one sensor.

Once the subject's activity, movement or motion has been identified or determined and brain activity measured 602, the next step is to identify or determine any motor symptoms of a movement disorder or side effects of a treatment 604 that the subject is experiencing as experienced while performing the identified or determined movement, motion or activity. The identification or determination of symptoms or side effects 604 is similarly capable of being performed by either a human user or automatically, or combination thereof. Much like the activity identification 602 above, the subject or a clinician may input into the system any known symptoms or side effects. Preferably though, the system automatically identifies or determines the symptoms or side effects based at least in part on the signal from the at least one movement sensor and/or the measured brain activity from the dual-mode DBS lead, and at least in part on the identified or determined activity, movement or motion. Again, a separate symptom and side effect identification algorithm may utilize the signal from the sensor and the identified activity to identify or determine what symptoms and/or side effects the subject is experiencing or has experienced. This algorithm may then present options to the subject or to a clinician, again either local or remote, for confirmation or to be rejected.

Next, the step of correlating identified activities, movements or motions and/or symptoms with measured brain activity 605 is performed using a processor and correlation algorithm. The external sensors for movement/motion and/or physiological data, and inputted data are correlated with measured brain activity in order to identify the portions of the brain that are responsible for causing certain motions and symptoms. This correlation data is preferably built up over time and stored in local and/or database memory to create a map of correlated data such that the system can identify movement and/or symptoms based on measured brain activity without external measured movement data and/or user input.

Next, the step of measuring and quantifying the identified or determined motor symptoms or side effects 606 of the subject based at least in part on the signal of the at least one movement sensor(s) and/or brain activity sensor(s). The movement disorder diagnostic device uses the signal of the at least one movement sensor(s) and/or brain activity sensor(s) to provide an objective measurement and quantification of the severity of the subject's motor symptoms or side effects while the subject performs the identified or determined activity, movement or motion. The measured and quantified motor symptoms or side effects may include specific movement disorder symptoms, side effects from medication and/or therapy, or combinations thereof. The trained scoring algorithms of the movement disorder diagnostic device perform various measurements and calculations to provide this objective quantification of the subject's motor symptoms.

Once the subject's motor symptoms or side effects have been measured and quantified 606, data corresponding to these measured and quantified motor symptoms is entered into a processor comprising an algorithm(s) 608 for automated analysis. Typically, the data corresponding to the measured and quantified motor symptoms is an objective score, as described herein, but may be represented in numerous ways and means. Preferably, the quantified motor symptom data is entered 608 directly and automatically into the processor and into the determination algorithm(s) without the need for any manual human intervention, such as keying in the data.

The processor and its algorithm(s) then analyze the measured and quantified motor symptom data and calculate a second level of therapy parameters or settings 610. This second level of parameters or settings preferably corresponds to a mode of therapy or treatment that addresses the subject's needs as determined based on the identified or determined activity and symptoms or side effects, and the measured and quantified motor symptom data and brain activity data, as well as other data, goals, or objectives. In other words, if the system determines that, for example, the subject is experiencing a very strong tremor while driving, such determination being made as a result of the identification of the subject's activity and symptoms as well as measurement of the subject's movement and brain activity and quantifying the severity of the tremor, the processor and algorithm would provide a second level of parameters or settings 610 that would reduce or minimize the tremor the subject is experiencing. As noted, the calculation of a second level of parameters or settings may be based on any number of constraints or desired results for the subject, not solely the immediate symptom or side effect the subject is experiencing. For example, if the subject's main concern is reducing or minimizing symptom occurrence and or severity, the processor and its algorithms will take this desired goal into account when calculating the second level of parameters or settings. Similarly, the calculation of settings or parameters may be based on a desired reduction or minimization of side effects from medication or the therapy. Also, the calculation may be made to balance multiple desired results, such as if a slightly higher rate of occurrence of symptoms is acceptable to the subject in exchange for a minimization of side effects. Other examples of desired results that may be used to determine the second level of therapy parameters or settings for all embodiments include, but are not limited to, a therapeutic window (in terms of time or some other factor) in which the subject most positively responds to therapy, battery life, and other such constraints that might be considered in terms of optimizing the therapy parameters or settings. In any determination, the subject and the clinician, physician or technician decide what the initial desired result is, and these desired results, goals, or constraints are programmed into the processor and its algorithm(s) for the decision making process. In some embodiments, the desired result, goal or constraints may be edited, either by a clinician, physician or technician, or by the subject, in order to allow the algorithm to most accurately analyze the data in light of the optimal treatment for the subject.

Once this second level of parameters or settings has been calculated by the processor and its algorithm(s) 610, the parameters or settings are then transmitted to the subject's therapy device 612 (e.g., DBS device). The therapy device preferably comprises at least one electronic component for receiving such signals, and uses this at least one component to receive a signal(s) comprising the second level of therapy parameters or settings from the diagnostic device. The parameters or settings may additionally be transmitted via wireless communication, as described herein, between the subject's therapy device and a remote location such as to a database or server for storage, or directly to a remote clinician physician or technician for optional review. Once the therapy device receives the transmitted 612 second level of therapy parameters or settings, the second level of parameters or settings is then entered in to the subject's therapy device 614 for the device to provide therapy according to those parameters or settings. As such, the new, second level of parameters or settings is programmed into the subject's therapy device, and the device then operates according to those new parameters or settings 614 and provides that newly determined course of therapy or treatment to the subject.

FIG. 24 depicts a flow chart representing a method embodiment of the present invention for tuning the movement disorder diagnostic device with new therapy parameters or settings. This particular embodiment is envisioned to be performed in an automated fashion, by an algorithm(s), with little or no interaction required with a clinician, physician or technician, and where the subject is located remotely from a clinician, physician or technician, such as at home. First, a subject is provided with a therapy device 650, such as a DBS therapy device. The DBS device further comprises at least one dual-mode lead comprising a plurality of contacts, each contact adapted to both provide stimulation to the subject's brain as well as to sense or acquire signals from the subject's brain, particularly to measure brain activity. The therapy device preferably has a first level of therapy parameters or settings already programmed or entered into the device.

Once the subject has the therapy device 650, typically implanted, the system then identifies or determines what, if any, activity, movement or motion the subject is performing and brain activity is measured and acquired 652. This identification or determination may be made based on the measured brain activity, and the brain activity is measured with a dual-mode DBS lead adapted to provide stimulation as well as measure signals from the subject. The identification or determination may be made by the subject who may manually input the new activity, movement or motion into the device, by a clinician either local or remote to the subject, who may interpret the signals from the at least one sensor and identify or determine the activity, movement or motion, or more preferably, automatically by the device. Automatic identification or determination of the subject's activity, movement, or motion is preferably performed by a processor contained within the device which may include a separate algorithm for activity identification or determination, or the measurement and quantification algorithms may be able to perform this function. Preferably, even where the subject or a clinician, physician or technician identifies or determines and inputs the activity, movement or motion, the device uses the signal from the at least one sensor to estimate or predict the activity, movement or motion, and allows the particular user to confirm the identified or determined activity, movement or motion or to override the suggested identification and input another. The system may provide the user with several options to select from as well, all based on the signals from the at least one sensor.

Once the subject's activity, movement or motion has been identified or determined and brain activity measured 652, the next step is to identify or determine any motor symptoms of a movement disorder or side effects of a treatment 654 that the subject is experiencing or has experienced while performing the identified or determined movement, motion or activity. The identification or determination of symptoms or side effects 654 is similarly capable of being performed by either a human user or automatically, or combination thereof. Much like the activity identification 652 above, the subject or a clinician may input into the system any known symptoms or side effects. Preferably though, the system automatically identifies or determines the symptoms or side effects based at least in part on the signal from the at least one movement sensor and/or the measured brain activity from the dual-mode DBS lead, and at least in part on the identified or determined activity, movement or motion. Again, a separate symptom and side effect identification algorithm may utilize the signal from the sensor and the identified activity to identify or determine what symptoms and/or side effects the subject is experiencing or has experienced. This algorithm may then present options to the subject or to a clinician, again either local or remote, for confirmation or to be rejected.

Next, the step of correlating identified activities, movements or motions and/or symptoms with measured brain activity 655 is performed using a processor and correlation algorithm. The external sensors for movement/motion and/or physiological data and inputted data are correlated with measured brain activity in order to identify the portions of the brain that are responsible for causing certain motions and symptoms. This correlation data is preferably built up over time and stored in local and/or database memory to create a map of correlated data such that the system can identify movement and/or symptoms based on measured brain activity without external measured movement data and/or user input.

Next, the step of measuring and quantifying the identified or determined motor symptoms or side effects 656 of the subject based at least in part on the signal of the at least one brain activity sensor(s). The movement disorder diagnostic device uses the signal of the at least one brain activity sensor(s) to provide an objective measurement and quantification of the severity of the subject's motor symptoms or side effects while the subject performs the identified or determined activity, movement or motion. The measured and quantified motor symptoms or side effects may include specific movement disorder symptoms, side effects from medication and/or therapy, or combinations thereof. The trained scoring algorithms of the movement disorder diagnostic device perform various measurements and calculations to provide this objective quantification of the subject's motor symptoms.

Once the subject's motor symptoms or side effects have been measured and quantified 656, data corresponding to these measured and quantified motor symptoms is entered into a processor comprising an algorithm(s) 658 for automated analysis. Typically, the data corresponding to the measured and quantified motor symptoms is an objective score, as described herein, but may be represented in numerous ways and means. Preferably, the quantified motor symptom data is entered 658 directly and automatically into the processor and into the determination algorithm(s) without the need for any manual human intervention, such as keying in the data.

The processor and its algorithm(s) then analyze the measured and quantified motor symptom data and calculate a second level of therapy parameters or settings 660. This second level of parameters or settings preferably corresponds to a mode of therapy or treatment that addresses the subject's needs as determined based on the identified or determined activity and symptoms or side effects, and the measured and quantified motor symptom data and brain activity data, as well as other data, goals, or objectives. In other words, if the system determines that, for example, the subject is experiencing a very strong tremor while driving, such determination being made as a result of the identification of the subject's activity and symptoms as well as measurement of the subject's movement and brain activity and quantifying the severity of the tremor, the processor and algorithm would provide a second level of parameters or settings 610 that would reduce or minimize the tremor the subject is experiencing. As noted, the calculation of a second level of parameters or settings may be based on any number of constraints or desired results for the subject, not solely the immediate symptom or side effect the subject is experiencing. For example, if the subject's main concern is reducing or minimizing symptom occurrence and or severity, the processor and its algorithms will take this desired goal into account when calculating the second level of parameters or settings. Similarly, the calculation of settings or parameters may be based on a desired reduction or minimization of side effects from medication or the therapy. Also, the calculation may be made to balance multiple desired results, such as if a slightly higher rate of occurrence of symptoms is acceptable to the subject in exchange for a minimization of side effects. Other examples of desired results that may be used to determine the second level of therapy parameters or settings for all embodiments include, but are not limited to, a therapeutic window (in terms of time or some other factor) in which the subject most positively responds to therapy, battery life, and other such constraints that might be considered in terms of optimizing the therapy parameters or settings. In any determination, the subject and the clinician, physician or technician decide what the initial desired result is, and these desired results, goals, or constraints are programmed into the processor and its algorithm(s) for the decision making process. In some embodiments, the desired result, goal or constraints may be edited, either by a clinician, physician or technician, or by the subject, in order to allow the algorithm to most accurately analyze the data in light of the optimal treatment for the subject.

Once this second level of parameters or settings has been calculated by the processor and its algorithm(s) 660, the parameters or settings are then transmitted to the subject's therapy device 612 (e.g., DBS device). The therapy device preferably comprises at least one electronic component for receiving such signals, and uses this at least one component to receive a signal(s) comprising the second level of therapy parameters or settings from the diagnostic device. The parameters or settings may additionally transmitted via wireless communication, as described herein, between the subject's therapy device and a remote location such as to a database or server for storage, or directly to a remote clinician physician or technician for optional review. Once the therapy device receives the transmitted 662 second level of therapy parameters or settings, the second level of parameters or settings is then entered in to the subject's therapy device 664 for the device to provide therapy according to those parameters or settings. As such, the new, second level of parameters or settings is programmed into the subject's therapy device, and the device then operates according to those new parameters or settings 664 and provides that newly determined course of therapy or treatment to the subject.

FIG. 25 is a flow chart depicting an embodiment of the testing and measurement process for obtaining data related to a subject's external body movement or motion and/or physiological data and correlating such with measured brain activity to map the areas of the subject's brain related to particular types of motion. As an initial point, a subject for purposes of the present embodiment must have a therapy device, typically an implanted DBS device, already surgically implanted or being implanted. Once the DBS device is disposed within the subject, a diagnostic device is provided for the subject to wear 700. Preferably the diagnostic device comprises at least one sensor for measuring external body motion and/or at least one sensor for measuring other physiological signals of the subject, for example, electrophysiological signals such as EMG, EEG, EOG, and the like. The DBS device also preferably has a first level of therapy parameters already programmed into it for operation to deliver therapeutic electrical stimulation to the subject according to the programmed first level of parameters. This first level of parameters may be clinically decided upon and set by a clinician, or it may be a semi-automatically or automatically provided set of parameters provided by algorithms for calculating suggested parameters, as described herein. The therapeutic electrical stimulation is provided to the subject via dual-mode DBS leads, each preferably with a plurality of contacts. Also preferably, each of these contacts is capable of both providing electrical field stimulation to the subject, but is also adapted to acquire signals from the subject's body, such as electrical or physiological signals. The fact that the contacts, or at least some thereof, are capable of both providing electrical therapy and receiving electrical signals is the basis of the dual-mode designation—they operate in both transmission and reception modes with respect to electrical signals. Once the subject has both a DBS or other therapy device and is wearing the diagnostic device, the subject performs 702 either a directed or instructed movement disorder test, or goes about performing activities of daily living. While the subject is moving, the diagnostic device measures the subject's external motion 704, using external body motion sensors, and/or other physiological signals, using physiological sensors, from the subject, and substantially simultaneously 708 measures the subject's brain activity 706 using the therapy device's (e.g., DBS device) implanted dual-mode leads with a plurality of contacts each adapted to both transmit signals (electrical impulses for therapeutic excitation of the brain) and to acquire signals (electrical signals from the brain indicating brain activity). The data is preferably obtained substantially simultaneously in order to capture the exact movement and exact brain activity that coincide with each other. Time lapses in the measurement may not be reconcilable given the temporal nature of the movement and brain activity, so substantially simultaneous measurement allows the system to more accurately carry out the step of correlating 710 the external body motion and/or physiological data with the measured brain activity. Continuous or repeated measurements allow for the correlations between specific movements (including normal and/or symptomatic movement) and excitation or activity of specific portions of the subject's brain to be mapped 712 to each other such that the system effectively learns which portions of the brain and can identify the portions or combination of brain structures (possibly down to the single-neuronal level) of the brain that is/are responsible for a given normal or symptomatic movement. Such brain mapping allows the system to provide finely tuned, highly specific and tailored electrical stimulation from the DBS unit and leads to excite or suppress activity of the specific brain portion or structure relevant to a symptoms symptomatic movement.

FIGS. 26A-B are images depicting results from a brain-mapping session related to the subject's external body motion. FIG. 26A depicts detected arm movement brain activity 750 that corresponds to areas of the brain that were activated during the commission or performance of various arm movements. Similarly, FIG. 26B depicts detected leg movement brain activity 760 during performance of leg movements. Typical brain mapping performed by measuring electrical brain activity from exterior electrodes or interior electrodes implanted either just on or inside the skull, or on the surface of the brain. The present invention instead utilizes the contacts location each DBS lead to not only transmit electrical stimulation therapy, but also to acquire signals from within the subject's brain, more accurately mapping the location of activated neurons. ‘During a mapping session, either clinical with prescribed movements or during normal activities of daily living, the dual-mode DBS leads acquired signals from the subject's brain indicating the brain wave activity at a given moment. That acquired brain wave activity is then correlated to the known or measured physical, external body movements and identified as, in the present case, arm brain wave activity 750 or leg brain wave activity 760. Thus, the system can identify the areas, regions, or pathways through multiple areas or regions of the subject's brain that are responsible at least partially, for particular movements, thus allowing for more targeted therapy parameters that address the subject's specific needs as well as working with other desired constraints for the operation of the therapy device.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit and scope of the invention. Thus, it is intended that the present invention cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents. 

What is claimed:
 1. A method of adjusting a treatment or therapy device to reduce a subject's symptoms of a movement disorder or side effects of treatment or therapy of a subject's movement disorder provided by the treatment or therapy device, the method comprising steps of: measuring a subject's brain activity during a first time period; simultaneously measuring during the first time period the subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder and calculating a first time period quantification of the severity of such symptoms or side effects; changing parameters of the subject's treatment or therapy based at least in part on the first time period quantification of the severity of the symptoms or side effects; measuring the subject's brain activity during a second time period; simultaneously measuring during the second time period the subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder and calculating a second time period quantification of the severity of such symptoms or side effects; determining the brain activity that minimizes the measured symptoms or side effects by correlating the measured symptoms or side effects and the first and second quantifications of severity with the simultaneously measured brain activity to map the part(s) of the subject's brain responsible for the symptoms or side effects; training a tuning algorithm comprised in a processor based on the determined brain activity that minimizes the measured symptoms or side effects; calculating, with the tuning algorithm, a suggested set of treatment or therapy parameters based at least in part on the measured brain activity, the calculated quantifications of severity, and measured symptoms or side effects, the suggested set of parameters adapted to be reviewed and approved by a clinician, physician, or technician; and entering approved treatment or therapy parameters into the treatment or therapy device such that the treatment or therapy device provides treatment or therapy to the subject based on the approved parameters.
 2. The method of claim 1, wherein the correlating is performed by a correlation algorithm comprised in the processor, the correlation algorithm being a machine learning algorithm.
 3. The method of claim 2, wherein the machine learning correlation algorithm is a supervised algorithm.
 4. The method of claim 2, wherein the brain activity measuring steps are performed by at least one dual-mode deep brain stimulation (DBS) lead with at least one contact capable of providing electrical stimulation and acquiring physiological signals.
 5. The method of claim 4, wherein the at least one dual-mode DBS lead is adapted to simultaneously provide electrical stimulation from one or more of the plurality of contacts on the lead and acquire electrophysiological signals from the subject from one or more of the plurality of contacts on the lead.
 6. The method of claim 1, further comprising the step of displaying, on a display device, the measured brain activity in the form of a 2D or 3D image of the subject's brain concurrently with a tuning map presenting the calculated quantification of severity of symptoms or side effects associated with the measured brain activity being displayed.
 7. The method of claim 1, further comprising the step of, after training the tuning algorithm, measuring the subject's brain activity during a third time period, and the calculated suggested set of therapy or treatment parameters is based at least in part on the brain activity measured during the third time period, but not on the measured symptoms or side effects.
 8. A method of adjusting a treatment or therapy device to reduce a subject's symptoms of a movement disorder or side effects of treatment or therapy of a subject's movement disorder provided by the treatment or therapy device, the method comprising steps of: measuring a subject's brain activity during a first time period; simultaneously measuring during the first time period the subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder and calculating a first time period quantification of the severity of such symptoms or side effects; changing parameters of the subject's treatment or therapy based at least in part on the first time period quantification of the severity of the symptoms or side effects; measuring the subject's brain activity during a second time period; simultaneously measuring during the second time period the subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder and calculating a second time period quantification of the severity of such symptoms or side effects; determining the brain activity that minimizes the measured symptoms or side effects by correlating the measured symptoms or side effects and the first and second quantifications of severity with the simultaneously measured brain activity to map the part(s) of the subject's brain responsible for the symptoms or side effects; training a tuning algorithm comprised in a processor based on the determined brain activity that minimizes the measured symptoms or side effects; calculating, with the tuning algorithm, a suggested set of treatment or therapy parameters based at least in part on the measured brain activity, the calculated quantifications of severity, and measured symptoms or side effects; and entering the suggested set of treatment or therapy parameters into the treatment or therapy device such that the treatment or therapy device provides treatment or therapy to the subject based on the suggested set of parameters.
 9. The method of claim 8, wherein the correlating is performed by a correlation algorithm comprised in a processor, the correlation algorithm being a machine learning algorithm.
 10. The method of claim 9, wherein the machine learning correlation algorithm is a supervised algorithm.
 11. The method of claim 9, wherein the brain activity measuring steps are performed by at least one dual-mode deep brain stimulation (DBS) lead with a plurality of contacts adapted to provide electrical stimulation and to acquire physiological signals.
 12. The method of claim 11, wherein the at least one dual-mode DBS lead is adapted to simultaneously provide electrical stimulation from one or more of the plurality of contacts on the lead and acquire electrophysiological signals from the subject from one or more of the plurality of contacts on the lead.
 13. The method of claim 8, further comprising the step of displaying, on a display device, the measured brain activity in the form of a 2D or 3D image of the subject's brain concurrently with a tuning map presenting the calculated quantification of severity of symptoms or side effects associated with the measured brain activity being displayed.
 14. The method of claim 8, further comprising the step of, after training the tuning algorithm, measuring the subject's brain activity during a third time period, and the calculated suggested set of therapy or treatment parameters is based at least in part on the brain activity measured during the third time period, but not on the measured symptoms or side effects.
 15. A method of adjusting a treatment or therapy device to reduce a subject's symptoms of a movement disorder or side effects of treatment or therapy of a subject's movement disorder provided by the treatment or therapy device, the method comprising steps of: measuring a subject's brain activity during a first time period; simultaneously measuring during the first time period the subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder and calculating a first time period quantification of the severity of such symptoms or side effects; changing parameters of the subject's treatment or therapy based at least in part on the first time period quantification of the severity of the symptoms or side effects; measuring the subject's brain activity during a second time period; simultaneously measuring during the second time period the subject's movement disorder symptoms or side effects of treatment or therapy of the subject's movement disorder and calculating a second time period quantification of the severity of such symptoms or side effects; determining the brain activity that minimizes the measured symptoms or side effects by correlating the measured symptoms or side effects and the first and second quantifications of severity with the simultaneously measured brain activity to map the part(s) of the subject's brain responsible for the symptoms or side effects; training a tuning algorithm comprised in a processor based on the determined brain activity that minimizes the measured symptoms or side effects; measuring the subject's brain activity during a third time period; calculating, with the tuning algorithm, a suggested set of treatment or therapy parameters based at least in part on the brain activity measured during the third time and the calculated quantifications of severity, period; and entering the suggested set of treatment or therapy parameters into the treatment or therapy device such that the treatment or therapy device provides treatment or therapy to the subject based on the suggested set of parameters.
 16. The method of claim 15, wherein the correlating is performed by a correlation algorithm comprised in the processor, the correlation algorithm being a machine learning algorithm.
 17. The method of claim 16, wherein the machine learning correlation algorithm is a supervised algorithm.
 18. The method of claim 16, wherein the brain activity measuring steps are performed by at least one dual-mode deep brain stimulation (DBS) lead with a plurality of contacts adapted to provide electrical stimulation and to acquire physiological signals.
 19. The method of claim 18, wherein the at least one dual-mode DBS lead is adapted to simultaneously provide electrical stimulation from one or more of the plurality of contacts on the lead and acquire electrophysiological signals from the subject from one or more of the plurality of contacts on the lead.
 20. The method of claim 15, further comprising the step of displaying, on a display device, the measured brain activity in the form of a 2D or 3D image of the subject's brain concurrently with a tuning map presenting the calculated quantification of severity of symptoms or side effects associated with the measured brain activity being displayed. 